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.
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.
2. DeepNPTS
source
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, **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.
**trainer_kwargs: int,
keyword trainer arguments inherited from PyTorch Lighning’s
trainer.
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.
*
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.*
Usage Example
import pandas as pd
import matplotlib.pyplot as plt
from neuralforecast import NeuralForecast
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()