All the NeuralForecast models are “global” because we train them with all the series from the input pd.DataFrame data Y_df, yet the optimization objective is, momentarily, “univariate” as it does not consider the interaction between the output predictions across time series. Like the StatsForecast library, core.NeuralForecast allows you to explore collections of models efficiently and contains functions for convenient wrangling of input and output pd.DataFrames predictions.

First we load the AirPassengers dataset such that you can run all the examples.

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

from neuralforecast.tsdataset import TimeSeriesDataset
from neuralforecast.utils import AirPassengersDF as Y_df

# Split train/test and declare time series dataset
Y_train_df = Y_df[Y_df.ds<='1959-12-31'] # 132 train
Y_test_df = Y_df[Y_df.ds>'1959-12-31']   # 12 test
dataset, *_ = TimeSeriesDataset.from_df(Y_train_df)

​
1. Automatic Forecasting

​
A. RNN-Based

source

​
AutoRNN

 AutoRNN (h, loss=MAE(), valid_loss=None, config=None,
          search_alg=<ray.tune.search.basic_variant.BasicVariantGenerator
          object at 0x7f53a8ccc6a0>, num_samples=10, refit_with_val=False,
          cpus=4, gpus=0, verbose=False, alias=None, backend='ray',
          callbacks=None)

*Class for Automatic Hyperparameter Optimization, it builds on top of ray to give access to a wide variety of hyperparameter optimization tools ranging from classic grid search, to Bayesian optimization and HyperBand algorithm.

The validation loss to be optimized is defined by the config['loss'] dictionary value, the config also contains the rest of the hyperparameter search space.

It is important to note that the success of this hyperparameter optimization heavily relies on a strong correlation between the validation and test periods.*

TypeDefaultDetails
hintForecast horizon
lossMAEMAE()Instantiated train loss class from losses collection.
valid_lossNoneTypeNoneInstantiated valid loss class from losses collection.
configNoneTypeNoneDictionary with ray.tune defined search space or function that takes an optuna trial and returns a configuration dict.
search_algBasicVariantGenerator<ray.tune.search.basic_variant.BasicVariantGenerator object at 0x7f53a8ccc6a0>For ray see https://docs.ray.io/en/latest/tune/api_docs/suggestion.html
For optuna see https://optuna.readthedocs.io/en/stable/reference/samplers/index.html.
num_samplesint10Number of hyperparameter optimization steps/samples.
refit_with_valboolFalseRefit of best model should preserve val_size.
cpusint4Number of cpus to use during optimization. Only used with ray tune.
gpusint0Number of gpus to use during optimization, default all available. Only used with ray tune.
verboseboolFalseTrack progress.
aliasNoneTypeNoneCustom name of the model.
backendstrrayBackend to use for searching the hyperparameter space, can be either ‘ray’ or ‘optuna’.
callbacksNoneTypeNoneList of functions to call during the optimization process.
ray reference: https://docs.ray.io/en/latest/tune/tutorials/tune-metrics.html
optuna reference: https://optuna.readthedocs.io/en/stable/tutorial/20_recipes/007_optuna_callback.html
# Use your own config or AutoRNN.default_config
config = dict(max_steps=2, val_check_steps=1, input_size=-1, encoder_hidden_size=8)
model = AutoRNN(h=12, config=config, num_samples=1, cpus=1)

model.fit(dataset=dataset)
y_hat = model.predict(dataset=dataset)

# Optuna
model = AutoRNN(h=12, config=None, num_samples=1, cpus=1, backend='optuna')
source

​
AutoLSTM

 AutoLSTM (h, loss=MAE(), valid_loss=None, config=None,
           search_alg=<ray.tune.search.basic_variant.BasicVariantGenerator
           object at 0x7f53a6e6c850>, num_samples=10,
           refit_with_val=False, cpus=4, gpus=0, verbose=False,
           alias=None, backend='ray', callbacks=None)

*Class for Automatic Hyperparameter Optimization, it builds on top of ray to give access to a wide variety of hyperparameter optimization tools ranging from classic grid search, to Bayesian optimization and HyperBand algorithm.

The validation loss to be optimized is defined by the config['loss'] dictionary value, the config also contains the rest of the hyperparameter search space.

It is important to note that the success of this hyperparameter optimization heavily relies on a strong correlation between the validation and test periods.*

TypeDefaultDetails
hintForecast horizon
lossMAEMAE()Instantiated train loss class from losses collection.
valid_lossNoneTypeNoneInstantiated valid loss class from losses collection.
configNoneTypeNoneDictionary with ray.tune defined search space or function that takes an optuna trial and returns a configuration dict.
search_algBasicVariantGenerator<ray.tune.search.basic_variant.BasicVariantGenerator object at 0x7f53a6e6c850>For ray see https://docs.ray.io/en/latest/tune/api_docs/suggestion.html
For optuna see https://optuna.readthedocs.io/en/stable/reference/samplers/index.html.
num_samplesint10Number of hyperparameter optimization steps/samples.
refit_with_valboolFalseRefit of best model should preserve val_size.
cpusint4Number of cpus to use during optimization. Only used with ray tune.
gpusint0Number of gpus to use during optimization, default all available. Only used with ray tune.
verboseboolFalseTrack progress.
aliasNoneTypeNoneCustom name of the model.
backendstrrayBackend to use for searching the hyperparameter space, can be either ‘ray’ or ‘optuna’.
callbacksNoneTypeNoneList of functions to call during the optimization process.
ray reference: https://docs.ray.io/en/latest/tune/tutorials/tune-metrics.html
optuna reference: https://optuna.readthedocs.io/en/stable/tutorial/20_recipes/007_optuna_callback.html
# Use your own config or AutoLSTM.default_config
config = dict(max_steps=2, val_check_steps=1, input_size=-1, encoder_hidden_size=8)
model = AutoLSTM(h=12, config=config, num_samples=1, cpus=1)

# Fit and predict
model.fit(dataset=dataset)
y_hat = model.predict(dataset=dataset)

# Optuna
model = AutoLSTM(h=12, config=None, backend='optuna')
source

​
AutoGRU

 AutoGRU (h, loss=MAE(), valid_loss=None, config=None,
          search_alg=<ray.tune.search.basic_variant.BasicVariantGenerator
          object at 0x7f53a97dbaf0>, num_samples=10, refit_with_val=False,
          cpus=4, gpus=0, verbose=False, alias=None, backend='ray',
          callbacks=None)

*Class for Automatic Hyperparameter Optimization, it builds on top of ray to give access to a wide variety of hyperparameter optimization tools ranging from classic grid search, to Bayesian optimization and HyperBand algorithm.

The validation loss to be optimized is defined by the config['loss'] dictionary value, the config also contains the rest of the hyperparameter search space.

It is important to note that the success of this hyperparameter optimization heavily relies on a strong correlation between the validation and test periods.*

TypeDefaultDetails
hintForecast horizon
lossMAEMAE()Instantiated train loss class from losses collection.
valid_lossNoneTypeNoneInstantiated valid loss class from losses collection.
configNoneTypeNoneDictionary with ray.tune defined search space or function that takes an optuna trial and returns a configuration dict.
search_algBasicVariantGenerator<ray.tune.search.basic_variant.BasicVariantGenerator object at 0x7f53a97dbaf0>For ray see https://docs.ray.io/en/latest/tune/api_docs/suggestion.html
For optuna see https://optuna.readthedocs.io/en/stable/reference/samplers/index.html.
num_samplesint10Number of hyperparameter optimization steps/samples.
refit_with_valboolFalseRefit of best model should preserve val_size.
cpusint4Number of cpus to use during optimization. Only used with ray tune.
gpusint0Number of gpus to use during optimization, default all available. Only used with ray tune.
verboseboolFalseTrack progress.
aliasNoneTypeNoneCustom name of the model.
backendstrrayBackend to use for searching the hyperparameter space, can be either ‘ray’ or ‘optuna’.
callbacksNoneTypeNoneList of functions to call during the optimization process.
ray reference: https://docs.ray.io/en/latest/tune/tutorials/tune-metrics.html
optuna reference: https://optuna.readthedocs.io/en/stable/tutorial/20_recipes/007_optuna_callback.html
# Use your own config or AutoGRU.default_config
config = dict(max_steps=2, val_check_steps=1, input_size=-1, encoder_hidden_size=8)
model = AutoGRU(h=12, config=config, num_samples=1, cpus=1)

# Fit and predict
model.fit(dataset=dataset)
y_hat = model.predict(dataset=dataset)

# Optuna
model = AutoGRU(h=12, config=None, backend='optuna')
source

​
AutoTCN

 AutoTCN (h, loss=MAE(), valid_loss=None, config=None,
          search_alg=<ray.tune.search.basic_variant.BasicVariantGenerator
          object at 0x7f53a8ca4940>, num_samples=10, refit_with_val=False,
          cpus=4, gpus=0, verbose=False, alias=None, backend='ray',
          callbacks=None)

*Class for Automatic Hyperparameter Optimization, it builds on top of ray to give access to a wide variety of hyperparameter optimization tools ranging from classic grid search, to Bayesian optimization and HyperBand algorithm.

The validation loss to be optimized is defined by the config['loss'] dictionary value, the config also contains the rest of the hyperparameter search space.

It is important to note that the success of this hyperparameter optimization heavily relies on a strong correlation between the validation and test periods.*

TypeDefaultDetails
hintForecast horizon
lossMAEMAE()Instantiated train loss class from losses collection.
valid_lossNoneTypeNoneInstantiated valid loss class from losses collection.
configNoneTypeNoneDictionary with ray.tune defined search space or function that takes an optuna trial and returns a configuration dict.
search_algBasicVariantGenerator<ray.tune.search.basic_variant.BasicVariantGenerator object at 0x7f53a8ca4940>For ray see https://docs.ray.io/en/latest/tune/api_docs/suggestion.html
For optuna see https://optuna.readthedocs.io/en/stable/reference/samplers/index.html.
num_samplesint10Number of hyperparameter optimization steps/samples.
refit_with_valboolFalseRefit of best model should preserve val_size.
cpusint4Number of cpus to use during optimization. Only used with ray tune.
gpusint0Number of gpus to use during optimization, default all available. Only used with ray tune.
verboseboolFalseTrack progress.
aliasNoneTypeNoneCustom name of the model.
backendstrrayBackend to use for searching the hyperparameter space, can be either ‘ray’ or ‘optuna’.
callbacksNoneTypeNoneList of functions to call during the optimization process.
ray reference: https://docs.ray.io/en/latest/tune/tutorials/tune-metrics.html
optuna reference: https://optuna.readthedocs.io/en/stable/tutorial/20_recipes/007_optuna_callback.html
# Use your own config or AutoTCN.default_config
config = dict(max_steps=2, val_check_steps=1, input_size=-1, encoder_hidden_size=8)
model = AutoTCN(h=12, config=config, num_samples=1, cpus=1)

# Fit and predict
model.fit(dataset=dataset)
y_hat = model.predict(dataset=dataset)

# Optuna
model = AutoTCN(h=12, config=None, backend='optuna')
source

​
AutoDeepAR

 AutoDeepAR (h, loss=DistributionLoss(), valid_loss=MQLoss(), config=None,
             search_alg=<ray.tune.search.basic_variant.BasicVariantGenerat
             or object at 0x7f53a8ca6b60>, num_samples=10,
             refit_with_val=False, cpus=4, gpus=0, verbose=False,
             alias=None, backend='ray', callbacks=None)

*Class for Automatic Hyperparameter Optimization, it builds on top of ray to give access to a wide variety of hyperparameter optimization tools ranging from classic grid search, to Bayesian optimization and HyperBand algorithm.

The validation loss to be optimized is defined by the config['loss'] dictionary value, the config also contains the rest of the hyperparameter search space.

It is important to note that the success of this hyperparameter optimization heavily relies on a strong correlation between the validation and test periods.*

TypeDefaultDetails
hintForecast horizon
lossDistributionLossDistributionLoss()Instantiated train loss class from losses collection.
valid_lossMQLossMQLoss()Instantiated valid loss class from losses collection.
configNoneTypeNoneDictionary with ray.tune defined search space or function that takes an optuna trial and returns a configuration dict.
search_algBasicVariantGenerator<ray.tune.search.basic_variant.BasicVariantGenerator object at 0x7f53a8ca6b60>For ray see https://docs.ray.io/en/latest/tune/api_docs/suggestion.html
For optuna see https://optuna.readthedocs.io/en/stable/reference/samplers/index.html.
num_samplesint10Number of hyperparameter optimization steps/samples.
refit_with_valboolFalseRefit of best model should preserve val_size.
cpusint4Number of cpus to use during optimization. Only used with ray tune.
gpusint0Number of gpus to use during optimization, default all available. Only used with ray tune.
verboseboolFalseTrack progress.
aliasNoneTypeNoneCustom name of the model.
backendstrrayBackend to use for searching the hyperparameter space, can be either ‘ray’ or ‘optuna’.
callbacksNoneTypeNoneList of functions to call during the optimization process.
ray reference: https://docs.ray.io/en/latest/tune/tutorials/tune-metrics.html
optuna reference: https://optuna.readthedocs.io/en/stable/tutorial/20_recipes/007_optuna_callback.html
# Use your own config or AutoNHITS.default_config
config = dict(max_steps=1, val_check_steps=1, input_size=12, lstm_hidden_size=8)
model = AutoDeepAR(h=12, config=config, num_samples=1, cpus=1)

# Fit and predict
model.fit(dataset=dataset)
y_hat = model.predict(dataset=dataset)

# Optuna
model = AutoDeepAR(h=12, config=None, backend='optuna')
source

​
AutoDilatedRNN

 AutoDilatedRNN (h, loss=MAE(), valid_loss=None, config=None,
                 search_alg=<ray.tune.search.basic_variant.BasicVariantGen
                 erator object at 0x7f53a8c992a0>, num_samples=10,
                 refit_with_val=False, cpus=4, gpus=0, verbose=False,
                 alias=None, backend='ray', callbacks=None)

*Class for Automatic Hyperparameter Optimization, it builds on top of ray to give access to a wide variety of hyperparameter optimization tools ranging from classic grid search, to Bayesian optimization and HyperBand algorithm.

The validation loss to be optimized is defined by the config['loss'] dictionary value, the config also contains the rest of the hyperparameter search space.

It is important to note that the success of this hyperparameter optimization heavily relies on a strong correlation between the validation and test periods.*

TypeDefaultDetails
hintForecast horizon
lossMAEMAE()Instantiated train loss class from losses collection.
valid_lossNoneTypeNoneInstantiated valid loss class from losses collection.
configNoneTypeNoneDictionary with ray.tune defined search space or function that takes an optuna trial and returns a configuration dict.
search_algBasicVariantGenerator<ray.tune.search.basic_variant.BasicVariantGenerator object at 0x7f53a8c992a0>For ray see https://docs.ray.io/en/latest/tune/api_docs/suggestion.html
For optuna see https://optuna.readthedocs.io/en/stable/reference/samplers/index.html.
num_samplesint10Number of hyperparameter optimization steps/samples.
refit_with_valboolFalseRefit of best model should preserve val_size.
cpusint4Number of cpus to use during optimization. Only used with ray tune.
gpusint0Number of gpus to use during optimization, default all available. Only used with ray tune.
verboseboolFalseTrack progress.
aliasNoneTypeNoneCustom name of the model.
backendstrrayBackend to use for searching the hyperparameter space, can be either ‘ray’ or ‘optuna’.
callbacksNoneTypeNoneList of functions to call during the optimization process.
ray reference: https://docs.ray.io/en/latest/tune/tutorials/tune-metrics.html
optuna reference: https://optuna.readthedocs.io/en/stable/tutorial/20_recipes/007_optuna_callback.html
# Use your own config or AutoDilatedRNN.default_config
config = dict(max_steps=2, val_check_steps=1, input_size=-1, encoder_hidden_size=8)
model = AutoDilatedRNN(h=12, config=config, num_samples=1, cpus=1)

# Fit and predict
model.fit(dataset=dataset)
y_hat = model.predict(dataset=dataset)

# Optuna
model = AutoDilatedRNN(h=12, config=None, backend='optuna')
source

​
AutoBiTCN

 AutoBiTCN (h, loss=MAE(), valid_loss=None, config=None,
            search_alg=<ray.tune.search.basic_variant.BasicVariantGenerato
            r object at 0x7f53a8c83940>, num_samples=10,
            refit_with_val=False, cpus=4, gpus=0, verbose=False,
            alias=None, backend='ray', callbacks=None)

*Class for Automatic Hyperparameter Optimization, it builds on top of ray to give access to a wide variety of hyperparameter optimization tools ranging from classic grid search, to Bayesian optimization and HyperBand algorithm.

The validation loss to be optimized is defined by the config['loss'] dictionary value, the config also contains the rest of the hyperparameter search space.

It is important to note that the success of this hyperparameter optimization heavily relies on a strong correlation between the validation and test periods.*

TypeDefaultDetails
hintForecast horizon
lossMAEMAE()Instantiated train loss class from losses collection.
valid_lossNoneTypeNoneInstantiated valid loss class from losses collection.
configNoneTypeNoneDictionary with ray.tune defined search space or function that takes an optuna trial and returns a configuration dict.
search_algBasicVariantGenerator<ray.tune.search.basic_variant.BasicVariantGenerator object at 0x7f53a8c83940>For ray see https://docs.ray.io/en/latest/tune/api_docs/suggestion.html
For optuna see https://optuna.readthedocs.io/en/stable/reference/samplers/index.html.
num_samplesint10Number of hyperparameter optimization steps/samples.
refit_with_valboolFalseRefit of best model should preserve val_size.
cpusint4Number of cpus to use during optimization. Only used with ray tune.
gpusint0Number of gpus to use during optimization, default all available. Only used with ray tune.
verboseboolFalseTrack progress.
aliasNoneTypeNoneCustom name of the model.
backendstrrayBackend to use for searching the hyperparameter space, can be either ‘ray’ or ‘optuna’.
callbacksNoneTypeNoneList of functions to call during the optimization process.
ray reference: https://docs.ray.io/en/latest/tune/tutorials/tune-metrics.html
optuna reference: https://optuna.readthedocs.io/en/stable/tutorial/20_recipes/007_optuna_callback.html
# Use your own config or AutoNHITS.default_config
config = dict(max_steps=1, val_check_steps=1, input_size=12, hidden_size=8)
model = AutoBiTCN(h=12, config=config, num_samples=1, cpus=1)

# Fit and predict
model.fit(dataset=dataset)
y_hat = model.predict(dataset=dataset)

# Optuna
model = AutoBiTCN(h=12, config=None, backend='optuna')

​
B. MLP-Based

source

​
AutoMLP

 AutoMLP (h, loss=MAE(), valid_loss=None, config=None,
          search_alg=<ray.tune.search.basic_variant.BasicVariantGenerator
          object at 0x7f53a6c5d600>, num_samples=10, refit_with_val=False,
          cpus=4, gpus=0, verbose=False, alias=None, backend='ray',
          callbacks=None)

*Class for Automatic Hyperparameter Optimization, it builds on top of ray to give access to a wide variety of hyperparameter optimization tools ranging from classic grid search, to Bayesian optimization and HyperBand algorithm.

The validation loss to be optimized is defined by the config['loss'] dictionary value, the config also contains the rest of the hyperparameter search space.

It is important to note that the success of this hyperparameter optimization heavily relies on a strong correlation between the validation and test periods.*

TypeDefaultDetails
hintForecast horizon
lossMAEMAE()Instantiated train loss class from losses collection.
valid_lossNoneTypeNoneInstantiated valid loss class from losses collection.
configNoneTypeNoneDictionary with ray.tune defined search space or function that takes an optuna trial and returns a configuration dict.
search_algBasicVariantGenerator<ray.tune.search.basic_variant.BasicVariantGenerator object at 0x7f53a6c5d600>For ray see https://docs.ray.io/en/latest/tune/api_docs/suggestion.html
For optuna see https://optuna.readthedocs.io/en/stable/reference/samplers/index.html.
num_samplesint10Number of hyperparameter optimization steps/samples.
refit_with_valboolFalseRefit of best model should preserve val_size.
cpusint4Number of cpus to use during optimization. Only used with ray tune.
gpusint0Number of gpus to use during optimization, default all available. Only used with ray tune.
verboseboolFalseTrack progress.
aliasNoneTypeNoneCustom name of the model.
backendstrrayBackend to use for searching the hyperparameter space, can be either ‘ray’ or ‘optuna’.
callbacksNoneTypeNoneList of functions to call during the optimization process.
ray reference: https://docs.ray.io/en/latest/tune/tutorials/tune-metrics.html
optuna reference: https://optuna.readthedocs.io/en/stable/tutorial/20_recipes/007_optuna_callback.html
# Use your own config or AutoMLP.default_config
config = dict(max_steps=2, val_check_steps=1, input_size=12, hidden_size=8)
model = AutoMLP(h=12, config=config, num_samples=1, cpus=1)

# Fit and predict
model.fit(dataset=dataset)
y_hat = model.predict(dataset=dataset)

# Optuna
model = AutoMLP(h=12, config=None, backend='optuna')
source

​
AutoNBEATS

 AutoNBEATS (h, loss=MAE(), valid_loss=None, config=None,
             search_alg=<ray.tune.search.basic_variant.BasicVariantGenerat
             or object at 0x7f53a97d4eb0>, num_samples=10,
             refit_with_val=False, cpus=4, gpus=0, verbose=False,
             alias=None, backend='ray', callbacks=None)

*Class for Automatic Hyperparameter Optimization, it builds on top of ray to give access to a wide variety of hyperparameter optimization tools ranging from classic grid search, to Bayesian optimization and HyperBand algorithm.

The validation loss to be optimized is defined by the config['loss'] dictionary value, the config also contains the rest of the hyperparameter search space.

It is important to note that the success of this hyperparameter optimization heavily relies on a strong correlation between the validation and test periods.*

TypeDefaultDetails
hintForecast horizon
lossMAEMAE()Instantiated train loss class from losses collection.
valid_lossNoneTypeNoneInstantiated valid loss class from losses collection.
configNoneTypeNoneDictionary with ray.tune defined search space or function that takes an optuna trial and returns a configuration dict.
search_algBasicVariantGenerator<ray.tune.search.basic_variant.BasicVariantGenerator object at 0x7f53a97d4eb0>For ray see https://docs.ray.io/en/latest/tune/api_docs/suggestion.html
For optuna see https://optuna.readthedocs.io/en/stable/reference/samplers/index.html.
num_samplesint10Number of hyperparameter optimization steps/samples.
refit_with_valboolFalseRefit of best model should preserve val_size.
cpusint4Number of cpus to use during optimization. Only used with ray tune.
gpusint0Number of gpus to use during optimization, default all available. Only used with ray tune.
verboseboolFalseTrack progress.
aliasNoneTypeNoneCustom name of the model.
backendstrrayBackend to use for searching the hyperparameter space, can be either ‘ray’ or ‘optuna’.
callbacksNoneTypeNoneList of functions to call during the optimization process.
ray reference: https://docs.ray.io/en/latest/tune/tutorials/tune-metrics.html
optuna reference: https://optuna.readthedocs.io/en/stable/tutorial/20_recipes/007_optuna_callback.html
# Use your own config or AutoNBEATS.default_config
config = dict(max_steps=2, val_check_steps=1, input_size=12,
              mlp_units=3*[[8, 8]])
model = AutoNBEATS(h=12, config=config, num_samples=1, cpus=1)

# Fit and predict
model.fit(dataset=dataset)
y_hat = model.predict(dataset=dataset)

# Optuna
model = AutoNBEATS(h=12, config=None, backend='optuna')
source

​
AutoNBEATSx

 AutoNBEATSx (h, loss=MAE(), valid_loss=None, config=None,
              search_alg=<ray.tune.search.basic_variant.BasicVariantGenera
              tor object at 0x7f53a7263e80>, num_samples=10,
              refit_with_val=False, cpus=4, gpus=0, verbose=False,
              alias=None, backend='ray', callbacks=None)

*Class for Automatic Hyperparameter Optimization, it builds on top of ray to give access to a wide variety of hyperparameter optimization tools ranging from classic grid search, to Bayesian optimization and HyperBand algorithm.

The validation loss to be optimized is defined by the config['loss'] dictionary value, the config also contains the rest of the hyperparameter search space.

It is important to note that the success of this hyperparameter optimization heavily relies on a strong correlation between the validation and test periods.*

TypeDefaultDetails
hintForecast horizon
lossMAEMAE()Instantiated train loss class from losses collection.
valid_lossNoneTypeNoneInstantiated valid loss class from losses collection.
configNoneTypeNoneDictionary with ray.tune defined search space or function that takes an optuna trial and returns a configuration dict.
search_algBasicVariantGenerator<ray.tune.search.basic_variant.BasicVariantGenerator object at 0x7f53a7263e80>For ray see https://docs.ray.io/en/latest/tune/api_docs/suggestion.html
For optuna see https://optuna.readthedocs.io/en/stable/reference/samplers/index.html.
num_samplesint10Number of hyperparameter optimization steps/samples.
refit_with_valboolFalseRefit of best model should preserve val_size.
cpusint4Number of cpus to use during optimization. Only used with ray tune.
gpusint0Number of gpus to use during optimization, default all available. Only used with ray tune.
verboseboolFalseTrack progress.
aliasNoneTypeNoneCustom name of the model.
backendstrrayBackend to use for searching the hyperparameter space, can be either ‘ray’ or ‘optuna’.
callbacksNoneTypeNoneList of functions to call during the optimization process.
ray reference: https://docs.ray.io/en/latest/tune/tutorials/tune-metrics.html
optuna reference: https://optuna.readthedocs.io/en/stable/tutorial/20_recipes/007_optuna_callback.html
# Use your own config or AutoNBEATS.default_config
config = dict(max_steps=2, val_check_steps=1, input_size=12,
              mlp_units=3*[[8, 8]])
model = AutoNBEATSx(h=12, config=config, num_samples=1, cpus=1)

# Fit and predict
model.fit(dataset=dataset)
y_hat = model.predict(dataset=dataset)

# Optuna
model = AutoNBEATSx(h=12, config=None, backend='optuna')
source

​
AutoNHITS

 AutoNHITS (h, loss=MAE(), valid_loss=None, config=None,
            search_alg=<ray.tune.search.basic_variant.BasicVariantGenerato
            r object at 0x7f53a98f4310>, num_samples=10,
            refit_with_val=False, cpus=4, gpus=0, verbose=False,
            alias=None, backend='ray', callbacks=None)

*Class for Automatic Hyperparameter Optimization, it builds on top of ray to give access to a wide variety of hyperparameter optimization tools ranging from classic grid search, to Bayesian optimization and HyperBand algorithm.

The validation loss to be optimized is defined by the config['loss'] dictionary value, the config also contains the rest of the hyperparameter search space.

It is important to note that the success of this hyperparameter optimization heavily relies on a strong correlation between the validation and test periods.*

TypeDefaultDetails
hintForecast horizon
lossMAEMAE()Instantiated train loss class from losses collection.
valid_lossNoneTypeNoneInstantiated valid loss class from losses collection.
configNoneTypeNoneDictionary with ray.tune defined search space or function that takes an optuna trial and returns a configuration dict.
search_algBasicVariantGenerator<ray.tune.search.basic_variant.BasicVariantGenerator object at 0x7f53a98f4310>For ray see https://docs.ray.io/en/latest/tune/api_docs/suggestion.html
For optuna see https://optuna.readthedocs.io/en/stable/reference/samplers/index.html.
num_samplesint10Number of hyperparameter optimization steps/samples.
refit_with_valboolFalseRefit of best model should preserve val_size.
cpusint4Number of cpus to use during optimization. Only used with ray tune.
gpusint0Number of gpus to use during optimization, default all available. Only used with ray tune.
verboseboolFalseTrack progress.
aliasNoneTypeNoneCustom name of the model.
backendstrrayBackend to use for searching the hyperparameter space, can be either ‘ray’ or ‘optuna’.
callbacksNoneTypeNoneList of functions to call during the optimization process.
ray reference: https://docs.ray.io/en/latest/tune/tutorials/tune-metrics.html
optuna reference: https://optuna.readthedocs.io/en/stable/tutorial/20_recipes/007_optuna_callback.html
# Use your own config or AutoNHITS.default_config
config = dict(max_steps=2, val_check_steps=1, input_size=12, 
              mlp_units=3 * [[8, 8]])
model = AutoNHITS(h=12, config=config, num_samples=1, cpus=1)

# Fit and predict
model.fit(dataset=dataset)
y_hat = model.predict(dataset=dataset)

# Optuna
model = AutoNHITS(h=12, config=None, backend='optuna')
source

​
AutoDLinear

 AutoDLinear (h, loss=MAE(), valid_loss=None, config=None,
              search_alg=<ray.tune.search.basic_variant.BasicVariantGenera
              tor object at 0x7f53a8c592a0>, num_samples=10,
              refit_with_val=False, cpus=4, gpus=0, verbose=False,
              alias=None, backend='ray', callbacks=None)

*Class for Automatic Hyperparameter Optimization, it builds on top of ray to give access to a wide variety of hyperparameter optimization tools ranging from classic grid search, to Bayesian optimization and HyperBand algorithm.

The validation loss to be optimized is defined by the config['loss'] dictionary value, the config also contains the rest of the hyperparameter search space.

It is important to note that the success of this hyperparameter optimization heavily relies on a strong correlation between the validation and test periods.*

TypeDefaultDetails
hintForecast horizon
lossMAEMAE()Instantiated train loss class from losses collection.
valid_lossNoneTypeNoneInstantiated valid loss class from losses collection.
configNoneTypeNoneDictionary with ray.tune defined search space or function that takes an optuna trial and returns a configuration dict.
search_algBasicVariantGenerator<ray.tune.search.basic_variant.BasicVariantGenerator object at 0x7f53a8c592a0>For ray see https://docs.ray.io/en/latest/tune/api_docs/suggestion.html
For optuna see https://optuna.readthedocs.io/en/stable/reference/samplers/index.html.
num_samplesint10Number of hyperparameter optimization steps/samples.
refit_with_valboolFalseRefit of best model should preserve val_size.
cpusint4Number of cpus to use during optimization. Only used with ray tune.
gpusint0Number of gpus to use during optimization, default all available. Only used with ray tune.
verboseboolFalseTrack progress.
aliasNoneTypeNoneCustom name of the model.
backendstrrayBackend to use for searching the hyperparameter space, can be either ‘ray’ or ‘optuna’.
callbacksNoneTypeNoneList of functions to call during the optimization process.
ray reference: https://docs.ray.io/en/latest/tune/tutorials/tune-metrics.html
optuna reference: https://optuna.readthedocs.io/en/stable/tutorial/20_recipes/007_optuna_callback.html
# Use your own config or AutoDLinear.default_config
config = dict(max_steps=2, val_check_steps=1, input_size=12)
model = AutoDLinear(h=12, config=config, num_samples=1, cpus=1)

# Fit and predict
model.fit(dataset=dataset)
y_hat = model.predict(dataset=dataset)

# Optuna
model = AutoDLinear(h=12, config=None, backend='optuna')
source

​
AutoNLinear

 AutoNLinear (h, loss=MAE(), valid_loss=None, config=None,
              search_alg=<ray.tune.search.basic_variant.BasicVariantGenera
              tor object at 0x7f53a8e1d000>, num_samples=10,
              refit_with_val=False, cpus=4, gpus=0, verbose=False,
              alias=None, backend='ray', callbacks=None)

*Class for Automatic Hyperparameter Optimization, it builds on top of ray to give access to a wide variety of hyperparameter optimization tools ranging from classic grid search, to Bayesian optimization and HyperBand algorithm.

The validation loss to be optimized is defined by the config['loss'] dictionary value, the config also contains the rest of the hyperparameter search space.

It is important to note that the success of this hyperparameter optimization heavily relies on a strong correlation between the validation and test periods.*

TypeDefaultDetails
hintForecast horizon
lossMAEMAE()Instantiated train loss class from losses collection.
valid_lossNoneTypeNoneInstantiated valid loss class from losses collection.
configNoneTypeNoneDictionary with ray.tune defined search space or function that takes an optuna trial and returns a configuration dict.
search_algBasicVariantGenerator<ray.tune.search.basic_variant.BasicVariantGenerator object at 0x7f53a8e1d000>For ray see https://docs.ray.io/en/latest/tune/api_docs/suggestion.html
For optuna see https://optuna.readthedocs.io/en/stable/reference/samplers/index.html.
num_samplesint10Number of hyperparameter optimization steps/samples.
refit_with_valboolFalseRefit of best model should preserve val_size.
cpusint4Number of cpus to use during optimization. Only used with ray tune.
gpusint0Number of gpus to use during optimization, default all available. Only used with ray tune.
verboseboolFalseTrack progress.
aliasNoneTypeNoneCustom name of the model.
backendstrrayBackend to use for searching the hyperparameter space, can be either ‘ray’ or ‘optuna’.
callbacksNoneTypeNoneList of functions to call during the optimization process.
ray reference: https://docs.ray.io/en/latest/tune/tutorials/tune-metrics.html
optuna reference: https://optuna.readthedocs.io/en/stable/tutorial/20_recipes/007_optuna_callback.html
# Use your own config or AutoNLinear.default_config
config = dict(max_steps=2, val_check_steps=1, input_size=12)
model = AutoNLinear(h=12, config=config, num_samples=1, cpus=1)

# Fit and predict
model.fit(dataset=dataset)
y_hat = model.predict(dataset=dataset)

# Optuna
model = AutoNLinear(h=12, config=None, backend='optuna')
source

​
AutoTiDE

 AutoTiDE (h, loss=MAE(), valid_loss=None, config=None,
           search_alg=<ray.tune.search.basic_variant.BasicVariantGenerator
           object at 0x7f53a691a260>, num_samples=10,
           refit_with_val=False, cpus=4, gpus=0, verbose=False,
           alias=None, backend='ray', callbacks=None)

*Class for Automatic Hyperparameter Optimization, it builds on top of ray to give access to a wide variety of hyperparameter optimization tools ranging from classic grid search, to Bayesian optimization and HyperBand algorithm.

The validation loss to be optimized is defined by the config['loss'] dictionary value, the config also contains the rest of the hyperparameter search space.

It is important to note that the success of this hyperparameter optimization heavily relies on a strong correlation between the validation and test periods.*

TypeDefaultDetails
hintForecast horizon
lossMAEMAE()Instantiated train loss class from losses collection.
valid_lossNoneTypeNoneInstantiated valid loss class from losses collection.
configNoneTypeNoneDictionary with ray.tune defined search space or function that takes an optuna trial and returns a configuration dict.
search_algBasicVariantGenerator<ray.tune.search.basic_variant.BasicVariantGenerator object at 0x7f53a691a260>For ray see https://docs.ray.io/en/latest/tune/api_docs/suggestion.html
For optuna see https://optuna.readthedocs.io/en/stable/reference/samplers/index.html.
num_samplesint10Number of hyperparameter optimization steps/samples.
refit_with_valboolFalseRefit of best model should preserve val_size.
cpusint4Number of cpus to use during optimization. Only used with ray tune.
gpusint0Number of gpus to use during optimization, default all available. Only used with ray tune.
verboseboolFalseTrack progress.
aliasNoneTypeNoneCustom name of the model.
backendstrrayBackend to use for searching the hyperparameter space, can be either ‘ray’ or ‘optuna’.
callbacksNoneTypeNoneList of functions to call during the optimization process.
ray reference: https://docs.ray.io/en/latest/tune/tutorials/tune-metrics.html
optuna reference: https://optuna.readthedocs.io/en/stable/tutorial/20_recipes/007_optuna_callback.html
# Use your own config or AutoTiDE.default_config
config = dict(max_steps=2, val_check_steps=1, input_size=12)
model = AutoTiDE(h=12, config=config, num_samples=1, cpus=1)

# Fit and predict
model.fit(dataset=dataset)
y_hat = model.predict(dataset=dataset)

# Optuna
model = AutoTiDE(h=12, config=None, backend='optuna')
source

​
AutoDeepNPTS

 AutoDeepNPTS (h, loss=MAE(), valid_loss=None, config=None,
               search_alg=<ray.tune.search.basic_variant.BasicVariantGener
               ator object at 0x7f53a8e2d450>, num_samples=10,
               refit_with_val=False, cpus=4, gpus=0, verbose=False,
               alias=None, backend='ray', callbacks=None)

*Class for Automatic Hyperparameter Optimization, it builds on top of ray to give access to a wide variety of hyperparameter optimization tools ranging from classic grid search, to Bayesian optimization and HyperBand algorithm.

The validation loss to be optimized is defined by the config['loss'] dictionary value, the config also contains the rest of the hyperparameter search space.

It is important to note that the success of this hyperparameter optimization heavily relies on a strong correlation between the validation and test periods.*

TypeDefaultDetails
hintForecast horizon
lossMAEMAE()Instantiated train loss class from losses collection.
valid_lossNoneTypeNoneInstantiated valid loss class from losses collection.
configNoneTypeNoneDictionary with ray.tune defined search space or function that takes an optuna trial and returns a configuration dict.
search_algBasicVariantGenerator<ray.tune.search.basic_variant.BasicVariantGenerator object at 0x7f53a8e2d450>For ray see https://docs.ray.io/en/latest/tune/api_docs/suggestion.html
For optuna see https://optuna.readthedocs.io/en/stable/reference/samplers/index.html.
num_samplesint10Number of hyperparameter optimization steps/samples.
refit_with_valboolFalseRefit of best model should preserve val_size.
cpusint4Number of cpus to use during optimization. Only used with ray tune.
gpusint0Number of gpus to use during optimization, default all available. Only used with ray tune.
verboseboolFalseTrack progress.
aliasNoneTypeNoneCustom name of the model.
backendstrrayBackend to use for searching the hyperparameter space, can be either ‘ray’ or ‘optuna’.
callbacksNoneTypeNoneList of functions to call during the optimization process.
ray reference: https://docs.ray.io/en/latest/tune/tutorials/tune-metrics.html
optuna reference: https://optuna.readthedocs.io/en/stable/tutorial/20_recipes/007_optuna_callback.html
# Use your own config or AutoDeepNPTS.default_config
config = dict(max_steps=2, val_check_steps=1, input_size=12)
model = AutoDeepNPTS(h=12, config=config, num_samples=1, cpus=1)

# Fit and predict
model.fit(dataset=dataset)
y_hat = model.predict(dataset=dataset)

# Optuna
model = AutoDeepNPTS(h=12, config=None, backend='optuna')
source

​
AutoKAN

 AutoKAN (h, loss=MAE(), valid_loss=None, config=None,
          search_alg=<ray.tune.search.basic_variant.BasicVariantGenerator
          object at 0x7f53a8e0b730>, num_samples=10, refit_with_val=False,
          cpus=4, gpus=0, verbose=False, alias=None, backend='ray',
          callbacks=None)

*Class for Automatic Hyperparameter Optimization, it builds on top of ray to give access to a wide variety of hyperparameter optimization tools ranging from classic grid search, to Bayesian optimization and HyperBand algorithm.

The validation loss to be optimized is defined by the config['loss'] dictionary value, the config also contains the rest of the hyperparameter search space.

It is important to note that the success of this hyperparameter optimization heavily relies on a strong correlation between the validation and test periods.*

TypeDefaultDetails
hintForecast horizon
lossMAEMAE()Instantiated train loss class from losses collection.
valid_lossNoneTypeNoneInstantiated valid loss class from losses collection.
configNoneTypeNoneDictionary with ray.tune defined search space or function that takes an optuna trial and returns a configuration dict.
search_algBasicVariantGenerator<ray.tune.search.basic_variant.BasicVariantGenerator object at 0x7f53a8e0b730>For ray see https://docs.ray.io/en/latest/tune/api_docs/suggestion.html
For optuna see https://optuna.readthedocs.io/en/stable/reference/samplers/index.html.
num_samplesint10Number of hyperparameter optimization steps/samples.
refit_with_valboolFalseRefit of best model should preserve val_size.
cpusint4Number of cpus to use during optimization. Only used with ray tune.
gpusint0Number of gpus to use during optimization, default all available. Only used with ray tune.
verboseboolFalseTrack progress.
aliasNoneTypeNoneCustom name of the model.
backendstrrayBackend to use for searching the hyperparameter space, can be either ‘ray’ or ‘optuna’.
callbacksNoneTypeNoneList of functions to call during the optimization process.
ray reference: https://docs.ray.io/en/latest/tune/tutorials/tune-metrics.html
optuna reference: https://optuna.readthedocs.io/en/stable/tutorial/20_recipes/007_optuna_callback.html
# Use your own config or AutoDeepNPTS.default_config
config = dict(max_steps=2, val_check_steps=1, input_size=12)
model = AutoKAN(h=12, config=config, num_samples=1, cpus=1)

# Fit and predict
model.fit(dataset=dataset)
y_hat = model.predict(dataset=dataset)

# Optuna
model = AutoKAN(h=12, config=None, backend='optuna')

NameError: name 'dataset' is not defined
---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In[21], line 6
      3 model = AutoKAN(h=12, config=config, num_samples=1, cpus=1)
      5 # Fit and predict
----> 6 model.fit(dataset=dataset)
      7 y_hat = model.predict(dataset=dataset)
      9 # Optuna

NameError: name 'dataset' is not defined

​
C. Transformer-Based

source

​
AutoTFT

 AutoTFT (h, loss=MAE(), valid_loss=None, config=None,
          search_alg=<ray.tune.search.basic_variant.BasicVariantGenerator
          object at 0x7f53a8dca530>, num_samples=10, refit_with_val=False,
          cpus=4, gpus=0, verbose=False, alias=None, backend='ray',
          callbacks=None)

*Class for Automatic Hyperparameter Optimization, it builds on top of ray to give access to a wide variety of hyperparameter optimization tools ranging from classic grid search, to Bayesian optimization and HyperBand algorithm.

The validation loss to be optimized is defined by the config['loss'] dictionary value, the config also contains the rest of the hyperparameter search space.

It is important to note that the success of this hyperparameter optimization heavily relies on a strong correlation between the validation and test periods.*

TypeDefaultDetails
hintForecast horizon
lossMAEMAE()Instantiated train loss class from losses collection.
valid_lossNoneTypeNoneInstantiated valid loss class from losses collection.
configNoneTypeNoneDictionary with ray.tune defined search space or function that takes an optuna trial and returns a configuration dict.
search_algBasicVariantGenerator<ray.tune.search.basic_variant.BasicVariantGenerator object at 0x7f53a8dca530>For ray see https://docs.ray.io/en/latest/tune/api_docs/suggestion.html
For optuna see https://optuna.readthedocs.io/en/stable/reference/samplers/index.html.
num_samplesint10Number of hyperparameter optimization steps/samples.
refit_with_valboolFalseRefit of best model should preserve val_size.
cpusint4Number of cpus to use during optimization. Only used with ray tune.
gpusint0Number of gpus to use during optimization, default all available. Only used with ray tune.
verboseboolFalseTrack progress.
aliasNoneTypeNoneCustom name of the model.
backendstrrayBackend to use for searching the hyperparameter space, can be either ‘ray’ or ‘optuna’.
callbacksNoneTypeNoneList of functions to call during the optimization process.
ray reference: https://docs.ray.io/en/latest/tune/tutorials/tune-metrics.html
optuna reference: https://optuna.readthedocs.io/en/stable/tutorial/20_recipes/007_optuna_callback.html
# Use your own config or AutoNHITS.default_config
config = dict(max_steps=1, val_check_steps=1, input_size=12, hidden_size=8)
model = AutoTFT(h=12, config=config, num_samples=1, cpus=1)

# Fit and predict
model.fit(dataset=dataset)
y_hat = model.predict(dataset=dataset)

# Optuna
model = AutoTFT(h=12, config=None, backend='optuna')
source

​
AutoVanillaTransformer

 AutoVanillaTransformer (h, loss=MAE(), valid_loss=None, config=None,
                         search_alg=<ray.tune.search.basic_variant.BasicVa
                         riantGenerator object at 0x7f53a8e2dde0>,
                         num_samples=10, refit_with_val=False, cpus=4,
                         gpus=0, verbose=False, alias=None, backend='ray',
                         callbacks=None)

*Class for Automatic Hyperparameter Optimization, it builds on top of ray to give access to a wide variety of hyperparameter optimization tools ranging from classic grid search, to Bayesian optimization and HyperBand algorithm.

The validation loss to be optimized is defined by the config['loss'] dictionary value, the config also contains the rest of the hyperparameter search space.

It is important to note that the success of this hyperparameter optimization heavily relies on a strong correlation between the validation and test periods.*

TypeDefaultDetails
hintForecast horizon
lossMAEMAE()Instantiated train loss class from losses collection.
valid_lossNoneTypeNoneInstantiated valid loss class from losses collection.
configNoneTypeNoneDictionary with ray.tune defined search space or function that takes an optuna trial and returns a configuration dict.
search_algBasicVariantGenerator<ray.tune.search.basic_variant.BasicVariantGenerator object at 0x7f53a8e2dde0>For ray see https://docs.ray.io/en/latest/tune/api_docs/suggestion.html
For optuna see https://optuna.readthedocs.io/en/stable/reference/samplers/index.html.
num_samplesint10Number of hyperparameter optimization steps/samples.
refit_with_valboolFalseRefit of best model should preserve val_size.
cpusint4Number of cpus to use during optimization. Only used with ray tune.
gpusint0Number of gpus to use during optimization, default all available. Only used with ray tune.
verboseboolFalseTrack progress.
aliasNoneTypeNoneCustom name of the model.
backendstrrayBackend to use for searching the hyperparameter space, can be either ‘ray’ or ‘optuna’.
callbacksNoneTypeNoneList of functions to call during the optimization process.
ray reference: https://docs.ray.io/en/latest/tune/tutorials/tune-metrics.html
optuna reference: https://optuna.readthedocs.io/en/stable/tutorial/20_recipes/007_optuna_callback.html
# Use your own config or AutoNHITS.default_config
config = dict(max_steps=1, val_check_steps=1, input_size=12, hidden_size=8)
model = AutoVanillaTransformer(h=12, config=config, num_samples=1, cpus=1)

# Fit and predict
model.fit(dataset=dataset)
y_hat = model.predict(dataset=dataset)

# Optuna
model = AutoVanillaTransformer(h=12, config=None, backend='optuna')
source

​
AutoInformer

 AutoInformer (h, loss=MAE(), valid_loss=None, config=None,
               search_alg=<ray.tune.search.basic_variant.BasicVariantGener
               ator object at 0x7f53a8de7610>, num_samples=10,
               refit_with_val=False, cpus=4, gpus=0, verbose=False,
               alias=None, backend='ray', callbacks=None)

*Class for Automatic Hyperparameter Optimization, it builds on top of ray to give access to a wide variety of hyperparameter optimization tools ranging from classic grid search, to Bayesian optimization and HyperBand algorithm.

The validation loss to be optimized is defined by the config['loss'] dictionary value, the config also contains the rest of the hyperparameter search space.

It is important to note that the success of this hyperparameter optimization heavily relies on a strong correlation between the validation and test periods.*

TypeDefaultDetails
hintForecast horizon
lossMAEMAE()Instantiated train loss class from losses collection.
valid_lossNoneTypeNoneInstantiated valid loss class from losses collection.
configNoneTypeNoneDictionary with ray.tune defined search space or function that takes an optuna trial and returns a configuration dict.
search_algBasicVariantGenerator<ray.tune.search.basic_variant.BasicVariantGenerator object at 0x7f53a8de7610>For ray see https://docs.ray.io/en/latest/tune/api_docs/suggestion.html
For optuna see https://optuna.readthedocs.io/en/stable/reference/samplers/index.html.
num_samplesint10Number of hyperparameter optimization steps/samples.
refit_with_valboolFalseRefit of best model should preserve val_size.
cpusint4Number of cpus to use during optimization. Only used with ray tune.
gpusint0Number of gpus to use during optimization, default all available. Only used with ray tune.
verboseboolFalseTrack progress.
aliasNoneTypeNoneCustom name of the model.
backendstrrayBackend to use for searching the hyperparameter space, can be either ‘ray’ or ‘optuna’.
callbacksNoneTypeNoneList of functions to call during the optimization process.
ray reference: https://docs.ray.io/en/latest/tune/tutorials/tune-metrics.html
optuna reference: https://optuna.readthedocs.io/en/stable/tutorial/20_recipes/007_optuna_callback.html
# Use your own config or AutoNHITS.default_config
config = dict(max_steps=1, val_check_steps=1, input_size=12, hidden_size=8)
model = AutoInformer(h=12, config=config, num_samples=1, cpus=1)

# Fit and predict
model.fit(dataset=dataset)
y_hat = model.predict(dataset=dataset)

# Optuna
model = AutoInformer(h=12, config=None, backend='optuna')
source

​
AutoAutoformer

 AutoAutoformer (h, loss=MAE(), valid_loss=None, config=None,
                 search_alg=<ray.tune.search.basic_variant.BasicVariantGen
                 erator object at 0x7f53a8dca950>, num_samples=10,
                 refit_with_val=False, cpus=4, gpus=0, verbose=False,
                 alias=None, backend='ray', callbacks=None)

*Class for Automatic Hyperparameter Optimization, it builds on top of ray to give access to a wide variety of hyperparameter optimization tools ranging from classic grid search, to Bayesian optimization and HyperBand algorithm.

The validation loss to be optimized is defined by the config['loss'] dictionary value, the config also contains the rest of the hyperparameter search space.

It is important to note that the success of this hyperparameter optimization heavily relies on a strong correlation between the validation and test periods.*

TypeDefaultDetails
hintForecast horizon
lossMAEMAE()Instantiated train loss class from losses collection.
valid_lossNoneTypeNoneInstantiated valid loss class from losses collection.
configNoneTypeNoneDictionary with ray.tune defined search space or function that takes an optuna trial and returns a configuration dict.
search_algBasicVariantGenerator<ray.tune.search.basic_variant.BasicVariantGenerator object at 0x7f53a8dca950>For ray see https://docs.ray.io/en/latest/tune/api_docs/suggestion.html
For optuna see https://optuna.readthedocs.io/en/stable/reference/samplers/index.html.
num_samplesint10Number of hyperparameter optimization steps/samples.
refit_with_valboolFalseRefit of best model should preserve val_size.
cpusint4Number of cpus to use during optimization. Only used with ray tune.
gpusint0Number of gpus to use during optimization, default all available. Only used with ray tune.
verboseboolFalseTrack progress.
aliasNoneTypeNoneCustom name of the model.
backendstrrayBackend to use for searching the hyperparameter space, can be either ‘ray’ or ‘optuna’.
callbacksNoneTypeNoneList of functions to call during the optimization process.
ray reference: https://docs.ray.io/en/latest/tune/tutorials/tune-metrics.html
optuna reference: https://optuna.readthedocs.io/en/stable/tutorial/20_recipes/007_optuna_callback.html
# Use your own config or AutoNHITS.default_config
config = dict(max_steps=1, val_check_steps=1, input_size=12, hidden_size=8)
model = AutoAutoformer(h=12, config=config, num_samples=1, cpus=1)

# Fit and predict
model.fit(dataset=dataset)
y_hat = model.predict(dataset=dataset)

# Optuna
model = AutoAutoformer(h=12, config=None, backend='optuna')
source

​
AutoFEDformer

 AutoFEDformer (h, loss=MAE(), valid_loss=None, config=None,
                search_alg=<ray.tune.search.basic_variant.BasicVariantGene
                rator object at 0x7f53a8dd8460>, num_samples=10,
                refit_with_val=False, cpus=4, gpus=0, verbose=False,
                alias=None, backend='ray', callbacks=None)

*Class for Automatic Hyperparameter Optimization, it builds on top of ray to give access to a wide variety of hyperparameter optimization tools ranging from classic grid search, to Bayesian optimization and HyperBand algorithm.

The validation loss to be optimized is defined by the config['loss'] dictionary value, the config also contains the rest of the hyperparameter search space.

It is important to note that the success of this hyperparameter optimization heavily relies on a strong correlation between the validation and test periods.*

TypeDefaultDetails
hintForecast horizon
lossMAEMAE()Instantiated train loss class from losses collection.
valid_lossNoneTypeNoneInstantiated valid loss class from losses collection.
configNoneTypeNoneDictionary with ray.tune defined search space or function that takes an optuna trial and returns a configuration dict.
search_algBasicVariantGenerator<ray.tune.search.basic_variant.BasicVariantGenerator object at 0x7f53a8dd8460>For ray see https://docs.ray.io/en/latest/tune/api_docs/suggestion.html
For optuna see https://optuna.readthedocs.io/en/stable/reference/samplers/index.html.
num_samplesint10Number of hyperparameter optimization steps/samples.
refit_with_valboolFalseRefit of best model should preserve val_size.
cpusint4Number of cpus to use during optimization. Only used with ray tune.
gpusint0Number of gpus to use during optimization, default all available. Only used with ray tune.
verboseboolFalseTrack progress.
aliasNoneTypeNoneCustom name of the model.
backendstrrayBackend to use for searching the hyperparameter space, can be either ‘ray’ or ‘optuna’.
callbacksNoneTypeNoneList of functions to call during the optimization process.
ray reference: https://docs.ray.io/en/latest/tune/tutorials/tune-metrics.html
optuna reference: https://optuna.readthedocs.io/en/stable/tutorial/20_recipes/007_optuna_callback.html
# Use your own config or AutoNHITS.default_config
config = dict(max_steps=1, val_check_steps=1, input_size=12, hidden_size=64)
model = AutoFEDformer(h=12, config=config, num_samples=1, cpus=1)

# Fit and predict
model.fit(dataset=dataset)
y_hat = model.predict(dataset=dataset)

# Optuna
model = AutoFEDformer(h=12, config=None, backend='optuna')
source

​
AutoPatchTST

 AutoPatchTST (h, loss=MAE(), valid_loss=None, config=None,
               search_alg=<ray.tune.search.basic_variant.BasicVariantGener
               ator object at 0x7f53a8e46ce0>, num_samples=10,
               refit_with_val=False, cpus=4, gpus=0, verbose=False,
               alias=None, backend='ray', callbacks=None)

*Class for Automatic Hyperparameter Optimization, it builds on top of ray to give access to a wide variety of hyperparameter optimization tools ranging from classic grid search, to Bayesian optimization and HyperBand algorithm.

The validation loss to be optimized is defined by the config['loss'] dictionary value, the config also contains the rest of the hyperparameter search space.

It is important to note that the success of this hyperparameter optimization heavily relies on a strong correlation between the validation and test periods.*

TypeDefaultDetails
hintForecast horizon
lossMAEMAE()Instantiated train loss class from losses collection.
valid_lossNoneTypeNoneInstantiated valid loss class from losses collection.
configNoneTypeNoneDictionary with ray.tune defined search space or function that takes an optuna trial and returns a configuration dict.
search_algBasicVariantGenerator<ray.tune.search.basic_variant.BasicVariantGenerator object at 0x7f53a8e46ce0>For ray see https://docs.ray.io/en/latest/tune/api_docs/suggestion.html
For optuna see https://optuna.readthedocs.io/en/stable/reference/samplers/index.html.
num_samplesint10Number of hyperparameter optimization steps/samples.
refit_with_valboolFalseRefit of best model should preserve val_size.
cpusint4Number of cpus to use during optimization. Only used with ray tune.
gpusint0Number of gpus to use during optimization, default all available. Only used with ray tune.
verboseboolFalseTrack progress.
aliasNoneTypeNoneCustom name of the model.
backendstrrayBackend to use for searching the hyperparameter space, can be either ‘ray’ or ‘optuna’.
callbacksNoneTypeNoneList of functions to call during the optimization process.
ray reference: https://docs.ray.io/en/latest/tune/tutorials/tune-metrics.html
optuna reference: https://optuna.readthedocs.io/en/stable/tutorial/20_recipes/007_optuna_callback.html
# Use your own config or AutoNHITS.default_config
config = dict(max_steps=1, val_check_steps=1, input_size=12, hidden_size=16)
model = AutoPatchTST(h=12, config=config, num_samples=1, cpus=1)

# Fit and predict
model.fit(dataset=dataset)
y_hat = model.predict(dataset=dataset)

# Optuna
model = AutoPatchTST(h=12, config=None, backend='optuna')
source

​
AutoiTransformer

 AutoiTransformer (h, n_series, loss=MAE(), valid_loss=None, config=None,
                   search_alg=<ray.tune.search.basic_variant.BasicVariantG
                   enerator object at 0x7f53a8e1c400>, num_samples=10,
                   refit_with_val=False, cpus=4, gpus=0, verbose=False,
                   alias=None, backend='ray', callbacks=None)

*Class for Automatic Hyperparameter Optimization, it builds on top of ray to give access to a wide variety of hyperparameter optimization tools ranging from classic grid search, to Bayesian optimization and HyperBand algorithm.

The validation loss to be optimized is defined by the config['loss'] dictionary value, the config also contains the rest of the hyperparameter search space.

It is important to note that the success of this hyperparameter optimization heavily relies on a strong correlation between the validation and test periods.*

TypeDefaultDetails
hintForecast horizon
n_series
lossMAEMAE()Instantiated train loss class from losses collection.
valid_lossNoneTypeNoneInstantiated valid loss class from losses collection.
configNoneTypeNoneDictionary with ray.tune defined search space or function that takes an optuna trial and returns a configuration dict.
search_algBasicVariantGenerator<ray.tune.search.basic_variant.BasicVariantGenerator object at 0x7f53a8e1c400>For ray see https://docs.ray.io/en/latest/tune/api_docs/suggestion.html
For optuna see https://optuna.readthedocs.io/en/stable/reference/samplers/index.html.
num_samplesint10Number of hyperparameter optimization steps/samples.
refit_with_valboolFalseRefit of best model should preserve val_size.
cpusint4Number of cpus to use during optimization. Only used with ray tune.
gpusint0Number of gpus to use during optimization, default all available. Only used with ray tune.
verboseboolFalseTrack progress.
aliasNoneTypeNoneCustom name of the model.
backendstrrayBackend to use for searching the hyperparameter space, can be either ‘ray’ or ‘optuna’.
callbacksNoneTypeNoneList of functions to call during the optimization process.
ray reference: https://docs.ray.io/en/latest/tune/tutorials/tune-metrics.html
optuna reference: https://optuna.readthedocs.io/en/stable/tutorial/20_recipes/007_optuna_callback.html
# Use your own config or AutoiTransformer.default_config
config = dict(max_steps=1, val_check_steps=1, input_size=12, hidden_size=16)
model = AutoiTransformer(h=12, n_series=1, config=config, num_samples=1, cpus=1)

# Fit and predict
model.fit(dataset=dataset)
y_hat = model.predict(dataset=dataset)

# Optuna
model = AutoiTransformer(h=12, n_series=1, config=None, backend='optuna')

​
D. CNN Based

source

​
AutoTimesNet

 AutoTimesNet (h, loss=MAE(), valid_loss=None, config=None,
               search_alg=<ray.tune.search.basic_variant.BasicVariantGener
               ator object at 0x7f53a8e464d0>, num_samples=10,
               refit_with_val=False, cpus=4, gpus=0, verbose=False,
               alias=None, backend='ray', callbacks=None)

*Class for Automatic Hyperparameter Optimization, it builds on top of ray to give access to a wide variety of hyperparameter optimization tools ranging from classic grid search, to Bayesian optimization and HyperBand algorithm.

The validation loss to be optimized is defined by the config['loss'] dictionary value, the config also contains the rest of the hyperparameter search space.

It is important to note that the success of this hyperparameter optimization heavily relies on a strong correlation between the validation and test periods.*

TypeDefaultDetails
hintForecast horizon
lossMAEMAE()Instantiated train loss class from losses collection.
valid_lossNoneTypeNoneInstantiated valid loss class from losses collection.
configNoneTypeNoneDictionary with ray.tune defined search space or function that takes an optuna trial and returns a configuration dict.
search_algBasicVariantGenerator<ray.tune.search.basic_variant.BasicVariantGenerator object at 0x7f53a8e464d0>For ray see https://docs.ray.io/en/latest/tune/api_docs/suggestion.html
For optuna see https://optuna.readthedocs.io/en/stable/reference/samplers/index.html.
num_samplesint10Number of hyperparameter optimization steps/samples.
refit_with_valboolFalseRefit of best model should preserve val_size.
cpusint4Number of cpus to use during optimization. Only used with ray tune.
gpusint0Number of gpus to use during optimization, default all available. Only used with ray tune.
verboseboolFalseTrack progress.
aliasNoneTypeNoneCustom name of the model.
backendstrrayBackend to use for searching the hyperparameter space, can be either ‘ray’ or ‘optuna’.
callbacksNoneTypeNoneList of functions to call during the optimization process.
ray reference: https://docs.ray.io/en/latest/tune/tutorials/tune-metrics.html
optuna reference: https://optuna.readthedocs.io/en/stable/tutorial/20_recipes/007_optuna_callback.html
# Use your own config or AutoTimesNet.default_config
config = dict(max_steps=1, val_check_steps=1, input_size=12, hidden_size=32)
model = AutoTimesNet(h=12, config=config, num_samples=1, cpus=1)

# Fit and predict
model.fit(dataset=dataset)
y_hat = model.predict(dataset=dataset)

# Optuna
model = AutoTimesNet(h=12, config=None, backend='optuna')

​
E. Multivariate

source

​
AutoStemGNN

 AutoStemGNN (h, n_series, loss=MAE(), valid_loss=None, config=None,
              search_alg=<ray.tune.search.basic_variant.BasicVariantGenera
              tor object at 0x7f53a91207c0>, num_samples=10,
              refit_with_val=False, cpus=4, gpus=0, verbose=False,
              alias=None, backend='ray', callbacks=None)

*Class for Automatic Hyperparameter Optimization, it builds on top of ray to give access to a wide variety of hyperparameter optimization tools ranging from classic grid search, to Bayesian optimization and HyperBand algorithm.

The validation loss to be optimized is defined by the config['loss'] dictionary value, the config also contains the rest of the hyperparameter search space.

It is important to note that the success of this hyperparameter optimization heavily relies on a strong correlation between the validation and test periods.*

TypeDefaultDetails
hintForecast horizon
n_series
lossMAEMAE()Instantiated train loss class from losses collection.
valid_lossNoneTypeNoneInstantiated valid loss class from losses collection.
configNoneTypeNoneDictionary with ray.tune defined search space or function that takes an optuna trial and returns a configuration dict.
search_algBasicVariantGenerator<ray.tune.search.basic_variant.BasicVariantGenerator object at 0x7f53a91207c0>For ray see https://docs.ray.io/en/latest/tune/api_docs/suggestion.html
For optuna see https://optuna.readthedocs.io/en/stable/reference/samplers/index.html.
num_samplesint10Number of hyperparameter optimization steps/samples.
refit_with_valboolFalseRefit of best model should preserve val_size.
cpusint4Number of cpus to use during optimization. Only used with ray tune.
gpusint0Number of gpus to use during optimization, default all available. Only used with ray tune.
verboseboolFalseTrack progress.
aliasNoneTypeNoneCustom name of the model.
backendstrrayBackend to use for searching the hyperparameter space, can be either ‘ray’ or ‘optuna’.
callbacksNoneTypeNoneList of functions to call during the optimization process.
ray reference: https://docs.ray.io/en/latest/tune/tutorials/tune-metrics.html
optuna reference: https://optuna.readthedocs.io/en/stable/tutorial/20_recipes/007_optuna_callback.html
# Use your own config or AutoStemGNN.default_config
config = dict(max_steps=1, val_check_steps=1, input_size=12)
model = AutoStemGNN(h=12, n_series=1, config=config, num_samples=1, cpus=1)

# Fit and predict
model.fit(dataset=dataset)
y_hat = model.predict(dataset=dataset)

# Optuna
model = AutoStemGNN(h=12, n_series=1, config=None, backend='optuna')
source

​
AutoHINT

 AutoHINT (cls_model, h, loss, valid_loss, S, config,
           search_alg=<ray.tune.search.basic_variant.BasicVariantGenerator
           object at 0x7f53a8d691e0>, num_samples=10, cpus=4, gpus=0,
           refit_with_val=False, verbose=False, alias=None, backend='ray',
           callbacks=None)

*Class for Automatic Hyperparameter Optimization, it builds on top of ray to give access to a wide variety of hyperparameter optimization tools ranging from classic grid search, to Bayesian optimization and HyperBand algorithm.

The validation loss to be optimized is defined by the config['loss'] dictionary value, the config also contains the rest of the hyperparameter search space.

It is important to note that the success of this hyperparameter optimization heavily relies on a strong correlation between the validation and test periods.*

TypeDefaultDetails
cls_modelPyTorch/PyTorchLightning modelSee neuralforecast.models collection here.
hintForecast horizon
lossPyTorch moduleInstantiated train loss class from losses collection.
valid_lossPyTorch moduleInstantiated valid loss class from losses collection.
S
configdict or callableDictionary with ray.tune defined search space or function that takes an optuna trial and returns a configuration dict.
search_algBasicVariantGenerator<ray.tune.search.basic_variant.BasicVariantGenerator object at 0x7f53a8d691e0>For ray see https://docs.ray.io/en/latest/tune/api_docs/suggestion.html
For optuna see https://optuna.readthedocs.io/en/stable/reference/samplers/index.html.
num_samplesint10Number of hyperparameter optimization steps/samples.
cpusint4Number of cpus to use during optimization. Only used with ray tune.
gpusint0Number of gpus to use during optimization, default all available. Only used with ray tune.
refit_with_valboolFalseRefit of best model should preserve val_size.
verboseboolFalseTrack progress.
aliasNoneTypeNoneCustom name of the model.
backendstrrayBackend to use for searching the hyperparameter space, can be either ‘ray’ or ‘optuna’.
callbacksNoneTypeNoneList of functions to call during the optimization process.
ray reference: https://docs.ray.io/en/latest/tune/tutorials/tune-metrics.html
optuna reference: https://optuna.readthedocs.io/en/stable/tutorial/20_recipes/007_optuna_callback.html
# Perform a simple hyperparameter optimization with 
# NHITS and then reconcile with HINT
from neuralforecast.losses.pytorch import GMM, sCRPS

base_config = dict(max_steps=1, val_check_steps=1, input_size=8)
base_model = AutoNHITS(h=4, loss=GMM(n_components=2, quantiles=quantiles), 
                       config=base_config, num_samples=1, cpus=1)
model = HINT(h=4, S=S_df.values,
             model=base_model,  reconciliation='MinTraceOLS')

model.fit(dataset=dataset)
y_hat = model.predict(dataset=hint_dataset)

# Perform a conjunct hyperparameter optimization with 
# NHITS + HINT reconciliation configurations
nhits_config = {
       "learning_rate": tune.choice([1e-3]),                                     # Initial Learning rate
       "max_steps": tune.choice([1]),                                            # Number of SGD steps
       "val_check_steps": tune.choice([1]),                                      # Number of steps between validation
       "input_size": tune.choice([5 * 12]),                                      # input_size = multiplier * horizon
       "batch_size": tune.choice([7]),                                           # Number of series in windows
       "windows_batch_size": tune.choice([256]),                                 # Number of windows in batch
       "n_pool_kernel_size": tune.choice([[2, 2, 2], [16, 8, 1]]),               # MaxPool's Kernelsize
       "n_freq_downsample": tune.choice([[168, 24, 1], [24, 12, 1], [1, 1, 1]]), # Interpolation expressivity ratios
       "activation": tune.choice(['ReLU']),                                      # Type of non-linear activation
       "n_blocks":  tune.choice([[1, 1, 1]]),                                    # Blocks per each 3 stacks
       "mlp_units":  tune.choice([[[512, 512], [512, 512], [512, 512]]]),        # 2 512-Layers per block for each stack
       "interpolation_mode": tune.choice(['linear']),                            # Type of multi-step interpolation
       "random_seed": tune.randint(1, 10),
       "reconciliation": tune.choice(['BottomUp', 'MinTraceOLS', 'MinTraceWLS'])
    }
model = AutoHINT(h=4, S=S_df.values,
                 cls_model=NHITS,
                 config=nhits_config,
                 loss=GMM(n_components=2, level=[80, 90]),
                 valid_loss=sCRPS(level=[80, 90]),
                 num_samples=1, cpus=1)
model.fit(dataset=dataset)
y_hat = model.predict(dataset=hint_dataset)
source

​
AutoTSMixer

 AutoTSMixer (h, n_series, loss=MAE(), valid_loss=None, config=None,
              search_alg=<ray.tune.search.basic_variant.BasicVariantGenera
              tor object at 0x7f53a8d8ccd0>, num_samples=10,
              refit_with_val=False, cpus=4, gpus=0, verbose=False,
              alias=None, backend='ray', callbacks=None)

*Class for Automatic Hyperparameter Optimization, it builds on top of ray to give access to a wide variety of hyperparameter optimization tools ranging from classic grid search, to Bayesian optimization and HyperBand algorithm.

The validation loss to be optimized is defined by the config['loss'] dictionary value, the config also contains the rest of the hyperparameter search space.

It is important to note that the success of this hyperparameter optimization heavily relies on a strong correlation between the validation and test periods.*

TypeDefaultDetails
hintForecast horizon
n_series
lossMAEMAE()Instantiated train loss class from losses collection.
valid_lossNoneTypeNoneInstantiated valid loss class from losses collection.
configNoneTypeNoneDictionary with ray.tune defined search space or function that takes an optuna trial and returns a configuration dict.
search_algBasicVariantGenerator<ray.tune.search.basic_variant.BasicVariantGenerator object at 0x7f53a8d8ccd0>For ray see https://docs.ray.io/en/latest/tune/api_docs/suggestion.html
For optuna see https://optuna.readthedocs.io/en/stable/reference/samplers/index.html.
num_samplesint10Number of hyperparameter optimization steps/samples.
refit_with_valboolFalseRefit of best model should preserve val_size.
cpusint4Number of cpus to use during optimization. Only used with ray tune.
gpusint0Number of gpus to use during optimization, default all available. Only used with ray tune.
verboseboolFalseTrack progress.
aliasNoneTypeNoneCustom name of the model.
backendstrrayBackend to use for searching the hyperparameter space, can be either ‘ray’ or ‘optuna’.
callbacksNoneTypeNoneList of functions to call during the optimization process.
ray reference: https://docs.ray.io/en/latest/tune/tutorials/tune-metrics.html
optuna reference: https://optuna.readthedocs.io/en/stable/tutorial/20_recipes/007_optuna_callback.html
# Use your own config or AutoTSMixer.default_config
config = dict(max_steps=1, val_check_steps=1, input_size=12)
model = AutoTSMixer(h=12, n_series=1, config=config, num_samples=1, cpus=1)

# Fit and predict
model.fit(dataset=dataset)
y_hat = model.predict(dataset=dataset)

# Optuna
model = AutoTSMixer(h=12, n_series=1, config=None, backend='optuna')
source

​
AutoTSMixerx

 AutoTSMixerx (h, n_series, loss=MAE(), valid_loss=None, config=None,
               search_alg=<ray.tune.search.basic_variant.BasicVariantGener
               ator object at 0x7f53a8d8e200>, num_samples=10,
               refit_with_val=False, cpus=4, gpus=0, verbose=False,
               alias=None, backend='ray', callbacks=None)

*Class for Automatic Hyperparameter Optimization, it builds on top of ray to give access to a wide variety of hyperparameter optimization tools ranging from classic grid search, to Bayesian optimization and HyperBand algorithm.

The validation loss to be optimized is defined by the config['loss'] dictionary value, the config also contains the rest of the hyperparameter search space.

It is important to note that the success of this hyperparameter optimization heavily relies on a strong correlation between the validation and test periods.*

TypeDefaultDetails
hintForecast horizon
n_series
lossMAEMAE()Instantiated train loss class from losses collection.
valid_lossNoneTypeNoneInstantiated valid loss class from losses collection.
configNoneTypeNoneDictionary with ray.tune defined search space or function that takes an optuna trial and returns a configuration dict.
search_algBasicVariantGenerator<ray.tune.search.basic_variant.BasicVariantGenerator object at 0x7f53a8d8e200>For ray see https://docs.ray.io/en/latest/tune/api_docs/suggestion.html
For optuna see https://optuna.readthedocs.io/en/stable/reference/samplers/index.html.
num_samplesint10Number of hyperparameter optimization steps/samples.
refit_with_valboolFalseRefit of best model should preserve val_size.
cpusint4Number of cpus to use during optimization. Only used with ray tune.
gpusint0Number of gpus to use during optimization, default all available. Only used with ray tune.
verboseboolFalseTrack progress.
aliasNoneTypeNoneCustom name of the model.
backendstrrayBackend to use for searching the hyperparameter space, can be either ‘ray’ or ‘optuna’.
callbacksNoneTypeNoneList of functions to call during the optimization process.
ray reference: https://docs.ray.io/en/latest/tune/tutorials/tune-metrics.html
optuna reference: https://optuna.readthedocs.io/en/stable/tutorial/20_recipes/007_optuna_callback.html
# Use your own config or AutoTSMixerx.default_config
config = dict(max_steps=1, val_check_steps=1, input_size=12)
model = AutoTSMixerx(h=12, n_series=1, config=config, num_samples=1, cpus=1)

# Fit and predict
model.fit(dataset=dataset)
y_hat = model.predict(dataset=dataset)

# Optuna
model = AutoTSMixerx(h=12, n_series=1, config=None, backend='optuna')
source

​
AutoMLPMultivariate

 AutoMLPMultivariate (h, n_series, loss=MAE(), valid_loss=None,
                      config=None, search_alg=<ray.tune.search.basic_varia
                      nt.BasicVariantGenerator object at 0x7f53a8db1330>,
                      num_samples=10, refit_with_val=False, cpus=4,
                      gpus=0, verbose=False, alias=None, backend='ray',
                      callbacks=None)

*Class for Automatic Hyperparameter Optimization, it builds on top of ray to give access to a wide variety of hyperparameter optimization tools ranging from classic grid search, to Bayesian optimization and HyperBand algorithm.

The validation loss to be optimized is defined by the config['loss'] dictionary value, the config also contains the rest of the hyperparameter search space.

It is important to note that the success of this hyperparameter optimization heavily relies on a strong correlation between the validation and test periods.*

TypeDefaultDetails
hintForecast horizon
n_series
lossMAEMAE()Instantiated train loss class from losses collection.
valid_lossNoneTypeNoneInstantiated valid loss class from losses collection.
configNoneTypeNoneDictionary with ray.tune defined search space or function that takes an optuna trial and returns a configuration dict.
search_algBasicVariantGenerator<ray.tune.search.basic_variant.BasicVariantGenerator object at 0x7f53a8db1330>For ray see https://docs.ray.io/en/latest/tune/api_docs/suggestion.html
For optuna see https://optuna.readthedocs.io/en/stable/reference/samplers/index.html.
num_samplesint10Number of hyperparameter optimization steps/samples.
refit_with_valboolFalseRefit of best model should preserve val_size.
cpusint4Number of cpus to use during optimization. Only used with ray tune.
gpusint0Number of gpus to use during optimization, default all available. Only used with ray tune.
verboseboolFalseTrack progress.
aliasNoneTypeNoneCustom name of the model.
backendstrrayBackend to use for searching the hyperparameter space, can be either ‘ray’ or ‘optuna’.
callbacksNoneTypeNoneList of functions to call during the optimization process.
ray reference: https://docs.ray.io/en/latest/tune/tutorials/tune-metrics.html
optuna reference: https://optuna.readthedocs.io/en/stable/tutorial/20_recipes/007_optuna_callback.html
# Use your own config or AutoTSMixerx.default_config
config = dict(max_steps=1, val_check_steps=1, input_size=12)
model = AutoMLPMultivariate(h=12, n_series=1, config=config, num_samples=1, cpus=1)

# Fit and predict
model.fit(dataset=dataset)
y_hat = model.predict(dataset=dataset)

# Optuna
model = AutoMLPMultivariate(h=12, n_series=1, config=None, backend='optuna')
source

​
AutoSOFTS

 AutoSOFTS (h, n_series, loss=MAE(), valid_loss=None, config=None,
            search_alg=<ray.tune.search.basic_variant.BasicVariantGenerato
            r object at 0x7f53a8e919c0>, num_samples=10,
            refit_with_val=False, cpus=4, gpus=0, verbose=False,
            alias=None, backend='ray', callbacks=None)

*Class for Automatic Hyperparameter Optimization, it builds on top of ray to give access to a wide variety of hyperparameter optimization tools ranging from classic grid search, to Bayesian optimization and HyperBand algorithm.

The validation loss to be optimized is defined by the config['loss'] dictionary value, the config also contains the rest of the hyperparameter search space.

It is important to note that the success of this hyperparameter optimization heavily relies on a strong correlation between the validation and test periods.*

TypeDefaultDetails
hintForecast horizon
n_series
lossMAEMAE()Instantiated train loss class from losses collection.
valid_lossNoneTypeNoneInstantiated valid loss class from losses collection.
configNoneTypeNoneDictionary with ray.tune defined search space or function that takes an optuna trial and returns a configuration dict.
search_algBasicVariantGenerator<ray.tune.search.basic_variant.BasicVariantGenerator object at 0x7f53a8e919c0>For ray see https://docs.ray.io/en/latest/tune/api_docs/suggestion.html
For optuna see https://optuna.readthedocs.io/en/stable/reference/samplers/index.html.
num_samplesint10Number of hyperparameter optimization steps/samples.
refit_with_valboolFalseRefit of best model should preserve val_size.
cpusint4Number of cpus to use during optimization. Only used with ray tune.
gpusint0Number of gpus to use during optimization, default all available. Only used with ray tune.
verboseboolFalseTrack progress.
aliasNoneTypeNoneCustom name of the model.
backendstrrayBackend to use for searching the hyperparameter space, can be either ‘ray’ or ‘optuna’.
callbacksNoneTypeNoneList of functions to call during the optimization process.
ray reference: https://docs.ray.io/en/latest/tune/tutorials/tune-metrics.html
optuna reference: https://optuna.readthedocs.io/en/stable/tutorial/20_recipes/007_optuna_callback.html
# Use your own config or AutoSOFTS.default_config
config = dict(max_steps=1, val_check_steps=1, input_size=12, hidden_size=16)
model = AutoSOFTS(h=12, n_series=1, config=config, num_samples=1, cpus=1)

# Fit and predict
model.fit(dataset=dataset)
y_hat = model.predict(dataset=dataset)

# Optuna
model = AutoSOFTS(h=12, n_series=1, config=None, backend='optuna')

2024-05-31 15:06:51,623 INFO worker.py:1749 -- Started a local Ray instance.
2024-05-31 15:06:52,217 INFO tune.py:263 -- Initializing Ray automatically. For cluster usage or custom Ray initialization, call `ray.init(...)` before `Tuner(...)`.
2024-05-31 15:06:52,219 INFO tune.py:624 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949
2024-05-31 15:06:54,633 ERROR tune_controller.py:1332 -- Trial task failed for trial _train_tune_eb814_00000
Traceback (most recent call last):
  File "/Users/marcopeix/miniconda3/envs/neuralforecast/lib/python3.10/site-packages/ray/air/execution/_internal/event_manager.py", line 110, in resolve_future
    result = ray.get(future)
  File "/Users/marcopeix/miniconda3/envs/neuralforecast/lib/python3.10/site-packages/ray/_private/auto_init_hook.py", line 21, in auto_init_wrapper
    return fn(*args, **kwargs)
  File "/Users/marcopeix/miniconda3/envs/neuralforecast/lib/python3.10/site-packages/ray/_private/client_mode_hook.py", line 103, in wrapper
    return func(*args, **kwargs)
  File "/Users/marcopeix/miniconda3/envs/neuralforecast/lib/python3.10/site-packages/ray/_private/worker.py", line 2623, in get
    values, debugger_breakpoint = worker.get_objects(object_refs, timeout=timeout)
  File "/Users/marcopeix/miniconda3/envs/neuralforecast/lib/python3.10/site-packages/ray/_private/worker.py", line 861, in get_objects
    raise value.as_instanceof_cause()
ray.exceptions.RayTaskError(ValueError): ray::ImplicitFunc.train() (pid=3951, ip=127.0.0.1, actor_id=1641e0248328a9ca364b098901000000, repr=_train_tune)
  File "/Users/marcopeix/miniconda3/envs/neuralforecast/lib/python3.10/site-packages/ray/tune/trainable/trainable.py", line 334, in train
    raise skipped from exception_cause(skipped)
  File "/Users/marcopeix/miniconda3/envs/neuralforecast/lib/python3.10/site-packages/ray/air/_internal/util.py", line 98, in run
    self._ret = self._target(*self._args, **self._kwargs)
  File "/Users/marcopeix/miniconda3/envs/neuralforecast/lib/python3.10/site-packages/ray/tune/trainable/function_trainable.py", line 53, in <lambda>
    training_func=lambda: self._trainable_func(self.config),
  File "/Users/marcopeix/miniconda3/envs/neuralforecast/lib/python3.10/site-packages/ray/tune/trainable/function_trainable.py", line 261, in _trainable_func
    output = fn()
  File "/Users/marcopeix/miniconda3/envs/neuralforecast/lib/python3.10/site-packages/ray/tune/trainable/util.py", line 130, in inner
    return trainable(config, **fn_kwargs)
  File "/Users/marcopeix/miniconda3/envs/neuralforecast/lib/python3.10/site-packages/neuralforecast/common/_base_auto.py", line 209, in _train_tune
    _ = self._fit_model(
  File "/Users/marcopeix/miniconda3/envs/neuralforecast/lib/python3.10/site-packages/neuralforecast/common/_base_auto.py", line 357, in _fit_model
    model = model.fit(
  File "/Users/marcopeix/miniconda3/envs/neuralforecast/lib/python3.10/site-packages/neuralforecast/common/_base_multivariate.py", line 541, in fit
    return self._fit(
  File "/Users/marcopeix/miniconda3/envs/neuralforecast/lib/python3.10/site-packages/neuralforecast/common/_base_model.py", line 281, in _fit
    trainer = pl.Trainer(**model.trainer_kwargs)
  File "/Users/marcopeix/miniconda3/envs/neuralforecast/lib/python3.10/site-packages/pytorch_lightning/utilities/argparse.py", line 70, in insert_env_defaults
    return fn(self, **kwargs)
  File "/Users/marcopeix/miniconda3/envs/neuralforecast/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 431, in __init__
    self._callback_connector.on_trainer_init(
  File "/Users/marcopeix/miniconda3/envs/neuralforecast/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/callback_connector.py", line 79, in on_trainer_init
    _validate_callbacks_list(self.trainer.callbacks)
  File "/Users/marcopeix/miniconda3/envs/neuralforecast/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/callback_connector.py", line 227, in _validate_callbacks_list
    stateful_callbacks = [cb for cb in callbacks if is_overridden("state_dict", instance=cb)]
  File "/Users/marcopeix/miniconda3/envs/neuralforecast/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/callback_connector.py", line 227, in <listcomp>
    stateful_callbacks = [cb for cb in callbacks if is_overridden("state_dict", instance=cb)]
  File "/Users/marcopeix/miniconda3/envs/neuralforecast/lib/python3.10/site-packages/pytorch_lightning/utilities/model_helpers.py", line 39, in is_overridden
    raise ValueError("Expected a parent")
ValueError: Expected a parent
2024-05-31 15:06:54,638 WARNING experiment_state.py:205 -- Experiment state snapshotting has been triggered multiple times in the last 5.0 seconds. A snapshot is forced if `CheckpointConfig(num_to_keep)` is set, and a trial has checkpointed >= `num_to_keep` times since the last snapshot.
You may want to consider increasing the `CheckpointConfig(num_to_keep)` or decreasing the frequency of saving checkpoints.
You can suppress this error by setting the environment variable TUNE_WARN_EXCESSIVE_EXPERIMENT_CHECKPOINT_SYNC_THRESHOLD_S to a smaller value than the current threshold (5.0).
2024-05-31 15:06:54,640 INFO tune.py:1021 -- Wrote the latest version of all result files and experiment state to '/Users/marcopeix/ray_results/_train_tune_2024-05-31_15-06-49' in 0.0031s.
2024-05-31 15:06:54,641 ERROR tune.py:1049 -- Trials did not complete: [_train_tune_eb814_00000]
Seed set to 1
GPU available: True (mps), used: True
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
`Trainer(val_check_interval=1)` was configured so validation will run after every batch.
`Trainer.fit` stopped: `max_steps=1` reached.
GPU available: True (mps), used: True
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
`Trainer(val_check_interval=1)` was configured so validation will run after every batch.
source

​
AutoTimeMixer

 AutoTimeMixer (h, n_series, loss=MAE(), valid_loss=None, config=None,
                search_alg=<ray.tune.search.basic_variant.BasicVariantGene
                rator object at 0x7f53a8d7c910>, num_samples=10,
                refit_with_val=False, cpus=4, gpus=0, verbose=False,
                alias=None, backend='ray', callbacks=None)

*Class for Automatic Hyperparameter Optimization, it builds on top of ray to give access to a wide variety of hyperparameter optimization tools ranging from classic grid search, to Bayesian optimization and HyperBand algorithm.

The validation loss to be optimized is defined by the config['loss'] dictionary value, the config also contains the rest of the hyperparameter search space.

It is important to note that the success of this hyperparameter optimization heavily relies on a strong correlation between the validation and test periods.*

TypeDefaultDetails
hintForecast horizon
n_series
lossMAEMAE()Instantiated train loss class from losses collection.
valid_lossNoneTypeNoneInstantiated valid loss class from losses collection.
configNoneTypeNoneDictionary with ray.tune defined search space or function that takes an optuna trial and returns a configuration dict.
search_algBasicVariantGenerator<ray.tune.search.basic_variant.BasicVariantGenerator object at 0x7f53a8d7c910>For ray see https://docs.ray.io/en/latest/tune/api_docs/suggestion.html
For optuna see https://optuna.readthedocs.io/en/stable/reference/samplers/index.html.
num_samplesint10Number of hyperparameter optimization steps/samples.
refit_with_valboolFalseRefit of best model should preserve val_size.
cpusint4Number of cpus to use during optimization. Only used with ray tune.
gpusint0Number of gpus to use during optimization, default all available. Only used with ray tune.
verboseboolFalseTrack progress.
aliasNoneTypeNoneCustom name of the model.
backendstrrayBackend to use for searching the hyperparameter space, can be either ‘ray’ or ‘optuna’.
callbacksNoneTypeNoneList of functions to call during the optimization process.
ray reference: https://docs.ray.io/en/latest/tune/tutorials/tune-metrics.html
optuna reference: https://optuna.readthedocs.io/en/stable/tutorial/20_recipes/007_optuna_callback.html
# Use your own config or AutoTimeMixer.default_config
config = dict(max_steps=1, val_check_steps=1, input_size=12, d_model=16)
model = AutoTimeMixer(h=12, n_series=1, config=config, num_samples=1, cpus=1)

# Fit and predict
model.fit(dataset=dataset)
y_hat = model.predict(dataset=dataset)

# Optuna
model = AutoTimeMixer(h=12, n_series=1, config=None, backend='optuna')

​
TESTS

Vanilla TransformerPyTorch Losses