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The TimesNet univariate model tackles the challenge of modeling multiple intraperiod and interperiod temporal variations. The architecture has the following distinctive features: - An embedding layer that maps the input sequence into a latent space. - Transformation of 1D time seires into 2D tensors, based on periods found by FFT. - A convolutional Inception block that captures temporal variations at different scales and between periods. References Figure 1. TimesNet Architecture. Figure 1. TimesNet Architecture.

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1. TimesNet

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TimesNet

TimesNet(
    h,
    input_size,
    stat_exog_list=None,
    hist_exog_list=None,
    futr_exog_list=None,
    exclude_insample_y=False,
    hidden_size=64,
    dropout=0.1,
    conv_hidden_size=64,
    top_k=5,
    num_kernels=6,
    encoder_layers=2,
    loss=MAE(),
    valid_loss=None,
    max_steps=1000,
    learning_rate=0.0001,
    num_lr_decays=-1,
    early_stop_patience_steps=-1,
    val_check_steps=100,
    batch_size=32,
    valid_batch_size=None,
    windows_batch_size=64,
    inference_windows_batch_size=256,
    start_padding_enabled=False,
    training_data_availability_threshold=0.0,
    step_size=1,
    scaler_type="standard",
    random_seed=1,
    drop_last_loader=False,
    alias=None,
    optimizer=None,
    optimizer_kwargs=None,
    lr_scheduler=None,
    lr_scheduler_kwargs=None,
    dataloader_kwargs=None,
    **trainer_kwargs
)
Bases: BaseModel TimesNet The TimesNet univariate model tackles the challenge of modeling multiple intraperiod and interperiod temporal variations. Parameters:
NameTypeDescriptionDefault
hintForecast horizon.required
input_sizeintLength of input window (lags).required
stat_exog_listlist of stroptional (default=None), Static exogenous columns.None
hist_exog_listlist of stroptional (default=None), Historic exogenous columns.None
futr_exog_listlist of stroptional (default=None), Future exogenous columns.None
exclude_insample_yboolThe model skips the autoregressive features y[t-input_size:t] if True.False
hidden_sizeintSize of embedding for embedding and encoders.64
dropoutfloatDropout for embeddings.0.1
conv_hidden_sizeintChannels of the Inception block.64
top_kintNumber of periods.5
num_kernelsintNumber of kernels for the Inception block.6
encoder_layersintNumber of encoder layers.2
lossPyTorch moduleInstantiated train loss class from losses collection.MAE()
valid_lossPyTorch moduleInstantiated validation loss class from losses collection.None
max_stepsintMaximum number of training steps.1000
learning_ratefloatLearning rate.0.0001
num_lr_decaysintNumber of learning rate decays, evenly distributed across max_steps. If -1, no learning rate decay is performed.-1
early_stop_patience_stepsintNumber of validation iterations before early stopping. If -1, no early stopping is performed.-1
val_check_stepsintNumber of training steps between every validation loss check.100
batch_sizeintNumber of different series in each batch.32
valid_batch_sizeintNumber of different series in each validation and test batch, if None uses batch_size.None
windows_batch_sizeintNumber of windows to sample in each training batch.64
inference_windows_batch_sizeintNumber of windows to sample in each inference batch.256
start_padding_enabledboolIf True, the model will pad the time series with zeros at the beginning by input size.False
training_data_availability_thresholdUnion[float, List[float]]minimum fraction of valid data points required for training windows. Single float applies to both insample and outsample; list of two floats specifies [insample_fraction, outsample_fraction]. Default 0.0 allows windows with only 1 valid data point (current behavior).0.0
step_sizeintStep size between each window of temporal data.1
scaler_typestrType of scaler for temporal inputs normalization see temporal scalers.‘standard’
random_seedintRandom_seed for pytorch initializer and numpy generators.1
drop_last_loaderboolIf True TimeSeriesDataLoader drops last non-full batch.False
aliasstroptional (default=None), Custom name of the model.None
optimizerSubclass of ‘torch.optim.Optimizer’optional (default=None), User specified optimizer instead of the default choice (Adam).None
optimizer_kwargsdictoptional (defualt=None), List of parameters used by the user specified optimizer.None
lr_schedulerSubclass of ‘torch.optim.lr_scheduler.LRScheduler’optional, user specified lr_scheduler instead of the default choice (StepLR).None
lr_scheduler_kwargsdictoptional, list of parameters used by the user specified lr_scheduler.None
dataloader_kwargsdictoptional (default=None), List of parameters passed into the PyTorch Lightning dataloader by the TimeSeriesDataLoader.None
**trainer_kwargsintkeyword trainer arguments inherited from PyTorch Lighning’s trainer.

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TimesNet.fit

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:
NameTypeDescriptionDefault
datasetTimeSeriesDatasetNeuralForecast’s TimeSeriesDataset, see documentation.required
val_sizeintValidation size for temporal cross-validation.0
random_seedintRandom seed for pytorch initializer and numpy generators, overwrites model.init’s.None
test_sizeintTest size for temporal cross-validation.0
Returns:
TypeDescription
None

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TimesNet.predict

predict(
    dataset,
    test_size=None,
    step_size=1,
    random_seed=None,
    quantiles=None,
    h=None,
    explainer_config=None,
    **data_module_kwargs
)
Predict. Neural network prediction with PL’s Trainer execution of predict_step. Parameters:
NameTypeDescriptionDefault
datasetTimeSeriesDatasetNeuralForecast’s TimeSeriesDataset, see documentation.required
test_sizeintTest size for temporal cross-validation.None
step_sizeintStep size between each window.1
random_seedintRandom seed for pytorch initializer and numpy generators, overwrites model.init’s.None
quantileslistTarget quantiles to predict.None
hintPrediction horizon, if None, uses the model’s fitted horizon. Defaults to None.None
explainer_configdictconfiguration for explanations.None
**data_module_kwargsdictPL’s TimeSeriesDataModule args, see documentation.
Returns:
TypeDescription
None

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Usage Example

import pandas as pd
import matplotlib.pyplot as plt

from neuralforecast import NeuralForecast
from neuralforecast.losses.pytorch import DistributionLoss
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

model = TimesNet(h=12,
                 input_size=24,
                 hidden_size = 16,
                 conv_hidden_size = 32,
                 loss=DistributionLoss(distribution='Normal', level=[80, 90]),
                 scaler_type='standard',
                 learning_rate=1e-3,
                 max_steps=100,
                 val_check_steps=50,
                 early_stop_patience_steps=2)

nf = NeuralForecast(
    models=[model],
    freq='ME'
)
nf.fit(df=Y_train_df, static_df=AirPassengersStatic, val_size=12)
forecasts = nf.predict(futr_df=Y_test_df)

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])

if model.loss.is_distribution_output:
    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['TimesNet-median'], c='blue', label='median')
    plt.fill_between(x=plot_df['ds'][-12:], 
                    y1=plot_df['TimesNet-lo-90'][-12:].values, 
                    y2=plot_df['TimesNet-hi-90'][-12:].values,
                    alpha=0.4, label='level 90')
    plt.grid()
    plt.legend()
    plt.plot()
else:
    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['TimesNet'], c='blue', label='Forecast')
    plt.legend()
    plt.grid()

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2. Auxiliary Functions

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Inception_Block_V1

Inception_Block_V1(in_channels, out_channels, num_kernels=6, init_weight=True)
Bases: Module Inception_Block_V1

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TimesBlock

TimesBlock(input_size, h, k, hidden_size, conv_hidden_size, num_kernels)
Bases: Module TimesBlock

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FFT_for_Period

FFT_for_Period(x, k=2)