SOFTS
1. Auxiliary functions
1.1 Embedding
source
DataEmbedding_inverted
DataEmbedding_inverted (c_in, d_model, dropout=0.1)
Data Embedding
1.2 STAD (STar Aggregate Dispatch)
source
STAD
STAD (d_series, d_core)
STar Aggregate Dispatch Module
2. Model
source
SOFTS
SOFTS (h, input_size, n_series, futr_exog_list=None, hist_exog_list=None, stat_exog_list=None, hidden_size:int=512, d_core:int=512, e_layers:int=2, d_ff:int=2048, dropout:float=0.1, use_norm:bool=True, loss=MAE(), valid_loss=None, max_steps:int=1000, learning_rate:float=0.001, num_lr_decays:int=-1, early_stop_patience_steps:int=-1, val_check_steps:int=100, batch_size:int=32, step_size:int=1, scaler_type:str='identity', random_seed:int=1, num_workers_loader:int=0, drop_last_loader:bool=False, optimizer=None, optimizer_kwargs=None, lr_scheduler=None, lr_scheduler_kwargs=None, **trainer_kwargs)
*SOFTS
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].
n_series: int, number of time-series.
futr_exog_list: str list, future exogenous columns.
hist_exog_list: str list, historic exogenous columns.
stat_exog_list: str list, static exogenous columns.
hidden_size:
int, dimension of the model.
d_core: int, dimension of core in
STAD.
e_layers: int, number of encoder layers.
d_ff: int,
dimension of fully-connected layer.
dropout: float, dropout
rate.
use_norm: bool, whether to normalize or not.
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.
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=1, 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.
SOFTS.fit
SOFTS.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.
test_size: int, test size for temporal cross-validation.
*
SOFTS.predict
SOFTS.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.
**data_module_kwargs: PL’s TimeSeriesDataModule args, see
documentation.*
3. Usage example
import pandas as pd
import matplotlib.pyplot as plt
from neuralforecast import NeuralForecast
from neuralforecast.models import SOFTS
from neuralforecast.utils import AirPassengersPanel, AirPassengersStatic
from neuralforecast.losses.pytorch import MSE
Y_train_df = AirPassengersPanel[AirPassengersPanel.ds<AirPassengersPanel['ds'].values[-12]].reset_index(drop=True) # 132 train
Y_test_df = AirPassengersPanel[AirPassengersPanel.ds>=AirPassengersPanel['ds'].values[-12]].reset_index(drop=True) # 12 test
model = SOFTS(h=12,
input_size=24,
n_series=2,
hidden_size=256,
d_core=256,
e_layers=2,
d_ff=64,
dropout=0.1,
use_norm=True,
loss=MSE(),
valid_loss=MAE(),
early_stop_patience_steps=3,
batch_size=32)
fcst = NeuralForecast(models=[model], freq='M')
fcst.fit(df=Y_train_df, static_df=AirPassengersStatic, val_size=12)
forecasts = fcst.predict(futr_df=Y_test_df)
# Plot predictions
fig, ax = plt.subplots(1, 1, figsize = (20, 7))
Y_hat_df = forecasts.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['SOFTS'], c='blue', label='Forecast')
ax.set_title('AirPassengers Forecast', fontsize=22)
ax.set_ylabel('Monthly Passengers', fontsize=20)
ax.set_xlabel('Year', fontsize=20)
ax.legend(prop={'size': 15})
ax.grid()