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 0x7f888ca5fe50>, 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 0x7f888ca5fe50>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 0x7f885c6c6dd0>, 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 0x7f885c6c6dd0>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 0x7f8864214820>, 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 0x7f8864214820>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 0x7f885c6bb4f0>, 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 0x7f885c6bb4f0>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 0x7f885c6c6920>, 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 0x7f885c6c6920>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 AutoDeepAR.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 0x7f885c665420>, 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 0x7f885c665420>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 0x7f885c6143a0>, 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 0x7f885c6143a0>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 AutoBiTCN.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 0x7f885c6786d0>, 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 0x7f885c6786d0>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 0x7f888cae2c20>, 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 0x7f888cae2c20>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 0x7f885c603220>, 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 0x7f885c603220>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 AutoNBEATSx.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 0x7f885c6595d0>, 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 0x7f885c6595d0>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 0x7f885c64d420>, 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 0x7f885c64d420>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 0x7f885c6140d0>, 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 0x7f885c6140d0>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 0x7f885d90d450>, 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 0x7f885d90d450>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 0x7f885d90de70>, 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 0x7f885d90de70>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')

​
C. KAN-Based

source

​
AutoKAN

 AutoKAN (h, loss=MAE(), valid_loss=None, config=None,
          search_alg=<ray.tune.search.basic_variant.BasicVariantGenerator
          object at 0x7f885c71b370>, 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 0x7f885c71b370>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 AutoKAN.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')

​
D. Transformer-Based

source

​
AutoTFT

 AutoTFT (h, loss=MAE(), valid_loss=None, config=None,
          search_alg=<ray.tune.search.basic_variant.BasicVariantGenerator
          object at 0x7f885c7dd6c0>, 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 0x7f885c7dd6c0>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 AutoTFT.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 0x7f885c6167d0>,
                         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 0x7f885c6167d0>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 AutoVanillaTransformer.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 0x7f885c7efdf0>, 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 0x7f885c7efdf0>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 AutoInformer.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 0x7f885c7dd0c0>, 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 0x7f885c7dd0c0>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 AutoAutoformer.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 0x7f885c7eca90>, 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 0x7f885c7eca90>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 AutoFEDFormer.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 0x7f885d90dcf0>, 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 0x7f885d90dcf0>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 AutoPatchTST.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 0x7f885ff69e40>, 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 0x7f885ff69e40>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')

​
E. CNN Based

source

​
AutoTimesNet

 AutoTimesNet (h, loss=MAE(), valid_loss=None, config=None,
               search_alg=<ray.tune.search.basic_variant.BasicVariantGener
               ator object at 0x7f885c761600>, 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 0x7f885c761600>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')

​
F. 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 0x7f885ffb11e0>, 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 0x7f885ffb11e0>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 0x7f885c7aa020>, 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 0x7f885c7aa020>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 0x7f885c76e8c0>, 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 0x7f885c76e8c0>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 0x7f885e58b430>, 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 0x7f885e58b430>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 0x7f885e520820>,
                      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 0x7f885e520820>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 AutoMLPMultivariate.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 0x7f885c75c0d0>, 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 0x7f885c75c0d0>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')
source

​
AutoTimeMixer

 AutoTimeMixer (h, n_series, loss=MAE(), valid_loss=None, config=None,
                search_alg=<ray.tune.search.basic_variant.BasicVariantGene
                rator object at 0x7f885d964d00>, 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 0x7f885d964d00>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')
source

​
AutoRMoK

 AutoRMoK (h, n_series, loss=MAE(), valid_loss=None, config=None,
           search_alg=<ray.tune.search.basic_variant.BasicVariantGenerator
           object at 0x7f885ff68b20>, 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 0x7f885ff68b20>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 AutoRMoK.default_config
config = dict(max_steps=1, val_check_steps=1, input_size=12, learning_rate=1e-2)
model = AutoRMoK(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 = AutoRMoK(h=12, n_series=1, config=None, backend='optuna')

​
TESTS

Vanilla TransformerPyTorch Losses