DeepNPTS

Deep Non-Parametric Time Series Forecaster (DeepNPTS) is a non-parametric baseline model for time-series forecasting. This model generates predictions by sampling from the empirical distribution according to a tunable strategy. This strategy is learned by exploiting the information across multiple related time series. This model provides a strong, simple baseline for time series forecasting.

References
Rangapuram, Syama Sundar, Jan Gasthaus, Lorenzo Stella, Valentin Flunkert, David Salinas, Yuyang Wang, and Tim Januschowski (2023). “Deep Non-Parametric Time Series Forecaster”. arXiv.

Losses

This implementation differs from the original work in that a weighted sum of the empirical distribution is returned as forecast. Therefore, it only supports point losses.

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DeepNPTS

 DeepNPTS (h, input_size:int=-1, hidden_size:int=32, batch_norm:bool=True,
           dropout:float=0.1, n_layers:int=2, futr_exog_list=None,
           hist_exog_list=None, stat_exog_list=None,
           exclude_insample_y=False, loss=MAE(), valid_loss=MAE(),
           max_steps:int=1000, learning_rate:float=0.001,
           num_lr_decays:int=3, early_stop_patience_steps:int=-1,
           val_check_steps:int=100, batch_size:int=32,
           valid_batch_size:Optional[int]=None,
           windows_batch_size:int=1024,
           inference_windows_batch_size:int=1024,
           start_padding_enabled=False, step_size:int=1,
           scaler_type:str='standard', random_seed:int=1,
           num_workers_loader=0, drop_last_loader=False, optimizer=None,
           optimizer_kwargs=None, lr_scheduler=None,
           lr_scheduler_kwargs=None, dataloader_kwargs=None,
           **trainer_kwargs)

*DeepNPTS

Deep Non-Parametric Time Series Forecaster (DeepNPTS) is a baseline model for time-series forecasting. This model generates predictions by (weighted) sampling from the empirical distribution according to a learnable strategy. The strategy is learned by exploiting the information across multiple related time series.

Parameters:
h: int, Forecast horizon.
input_size: int, autorregresive inputs size, y=[1,2,3,4] input_size=2 -> y_[t-2:t]=[1,2].
hidden_size: int=32, hidden size of dense layers.
batch_norm: bool=True, if True, applies Batch Normalization after each dense layer in the network.
dropout: float=0.1, dropout.
n_layers: int=2, number of dense layers.
stat_exog_list: str list, static exogenous columns.
hist_exog_list: str list, historic exogenous columns.
futr_exog_list: str list, future exogenous columns.
exclude_insample_y: bool=False, the model skips the autoregressive features y[t-input_size:t] if True.
loss: PyTorch module, instantiated train loss class from losses collection.
valid_loss: PyTorch module=loss, instantiated valid loss class from losses collection.
max_steps: int=1000, maximum number of training steps.
learning_rate: float=1e-3, Learning rate between (0, 1).
num_lr_decays: int=-1, Number of learning rate decays, evenly distributed across max_steps.
early_stop_patience_steps: int=-1, Number of validation iterations before early stopping.
val_check_steps: int=100, Number of training steps between every validation loss check.
batch_size: int=32, number of different series in each batch.
valid_batch_size: int=None, number of different series in each validation and test batch, if None uses batch_size.
windows_batch_size: int=1024, number of windows to sample in each training batch, default uses all.
inference_windows_batch_size: int=-1, number of windows to sample in each inference batch, -1 uses all.
start_padding_enabled: bool=False, if True, the model will pad the time series with zeros at the beginning, by input size.
step_size: int=1, step size between each window of temporal data.
scaler_type: str=‘identity’, type of scaler for temporal inputs normalization see temporal scalers.
random_seed: int, random_seed for pytorch initializer and numpy generators.
num_workers_loader: int=os.cpu_count(), workers to be used by TimeSeriesDataLoader.
drop_last_loader: bool=False, if True TimeSeriesDataLoader drops last non-full batch.
alias: str, optional, Custom name of the model.
optimizer: Subclass of ‘torch.optim.Optimizer’, optional, user specified optimizer instead of the default choice (Adam).
optimizer_kwargs: dict, optional, list of parameters used by the user specified optimizer.
lr_scheduler: Subclass of ‘torch.optim.lr_scheduler.LRScheduler’, optional, user specified lr_scheduler instead of the default choice (StepLR).
lr_scheduler_kwargs: dict, optional, list of parameters used by the user specified lr_scheduler.
dataloader_kwargs: dict, optional, list of parameters passed into the PyTorch Lightning dataloader by the TimeSeriesDataLoader.
**trainer_kwargs: int, keyword trainer arguments inherited from PyTorch Lighning’s trainer.

References
- Rangapuram, Syama Sundar, Jan Gasthaus, Lorenzo Stella, Valentin Flunkert, David Salinas, Yuyang Wang, and Tim Januschowski (2023). “Deep Non-Parametric Time Series Forecaster”. arXiv.
*

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DeepNPTS.fit

 DeepNPTS.fit (dataset, val_size=0, test_size=0, random_seed=None,
               distributed_config=None)

*Fit.

The fit method, optimizes the neural network’s weights using the initialization parameters (learning_rate, windows_batch_size, …) and the loss function as defined during the initialization. Within fit we use a PyTorch Lightning Trainer that inherits the initialization’s self.trainer_kwargs, to customize its inputs, see PL’s trainer arguments.

The method is designed to be compatible with SKLearn-like classes and in particular to be compatible with the StatsForecast library.

By default the model is not saving training checkpoints to protect disk memory, to get them change enable_checkpointing=True in __init__.

Parameters:
dataset: NeuralForecast’s TimeSeriesDataset, see documentation.
val_size: int, validation size for temporal cross-validation.
random_seed: int=None, random_seed for pytorch initializer and numpy generators, overwrites model.__init__’s.
test_size: int, test size for temporal cross-validation.
*

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DeepNPTS.predict

 DeepNPTS.predict (dataset, test_size=None, step_size=1, random_seed=None,
                   **data_module_kwargs)

*Predict.

Neural network prediction with PL’s Trainer execution of predict_step.

Parameters:
dataset: NeuralForecast’s TimeSeriesDataset, see documentation.
test_size: int=None, test size for temporal cross-validation.
step_size: int=1, Step size between each window.
random_seed: int=None, random_seed for pytorch initializer and numpy generators, overwrites model.__init__’s.
**data_module_kwargs: PL’s TimeSeriesDataModule args, see documentation.*

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Usage Example

import pandas as pd
import matplotlib.pyplot as plt

from neuralforecast import NeuralForecast
from neuralforecast.models import DeepNPTS
from neuralforecast.utils import AirPassengersPanel, AirPassengersStatic

Y_train_df = AirPassengersPanel[AirPassengersPanel.ds<AirPassengersPanel['ds'].values[-12]] # 132 train
Y_test_df = AirPassengersPanel[AirPassengersPanel.ds>=AirPassengersPanel['ds'].values[-12]].reset_index(drop=True) # 12 test

nf = NeuralForecast(
    models=[DeepNPTS(h=12,
                   input_size=24,
                   stat_exog_list=['airline1'],
                   futr_exog_list=['trend'],
                   max_steps=1000,
                   val_check_steps=10,
                   early_stop_patience_steps=3,
                   scaler_type='robust',
                   enable_progress_bar=True),
    ],
    freq='M'
)
nf.fit(df=Y_train_df, static_df=AirPassengersStatic, val_size=12)
Y_hat_df = nf.predict(futr_df=Y_test_df)

# Plot quantile predictions
Y_hat_df = Y_hat_df.reset_index(drop=False).drop(columns=['unique_id','ds'])
plot_df = pd.concat([Y_test_df, Y_hat_df], axis=1)
plot_df = pd.concat([Y_train_df, plot_df])

plot_df = plot_df[plot_df.unique_id=='Airline1'].drop('unique_id', axis=1)
plt.plot(plot_df['ds'], plot_df['y'], c='black', label='True')
plt.plot(plot_df['ds'], plot_df['DeepNPTS'], c='red', label='mean')
plt.grid()
plt.plot()