TimesNet

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
- Haixu Wu and Tengge Hu and Yong Liu and Hang Zhou and Jianmin Wang and Mingsheng Long. TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis - Based on the implementation in https://github.com/thuml/Time-Series-Library (license: https://github.com/thuml/Time-Series-Library/blob/main/LICENSE)

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1. Auxiliary Functions

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Inception_Block_V1

 Inception_Block_V1 (in_channels, out_channels, num_kernels=6,
                     init_weight=True)

*Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing to nest them in a tree structure. You can assign the submodules as regular attributes::

import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will have their parameters converted too when you call :meth:to, etc.

.. note:: As per the example above, an __init__() call to the parent class must be made before assignment on the child.

:ivar training: Boolean represents whether this module is in training or evaluation mode. :vartype training: bool*

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TimesBlock

 TimesBlock (input_size, h, k, hidden_size, conv_hidden_size, num_kernels)

*Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing to nest them in a tree structure. You can assign the submodules as regular attributes::

import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will have their parameters converted too when you call :meth:to, etc.

.. note:: As per the example above, an __init__() call to the parent class must be made before assignment on the child.

:ivar training: Boolean represents whether this module is in training or evaluation mode. :vartype training: bool*

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FFT_for_Period

 FFT_for_Period (x, k=2)

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2. TimesNet

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TimesNet

 TimesNet (h:int, input_size:int, stat_exog_list=None,
           hist_exog_list=None, futr_exog_list=None,
           exclude_insample_y=False, hidden_size:int=64,
           dropout:float=0.1, conv_hidden_size:int=64, top_k:int=5,
           num_kernels:int=6, encoder_layers:int=2, loss=MAE(),
           valid_loss=None, max_steps:int=1000,
           learning_rate:float=0.0001, num_lr_decays:int=-1,
           early_stop_patience_steps:int=-1, val_check_steps:int=100,
           batch_size:int=32, valid_batch_size:Optional[int]=None,
           windows_batch_size=64, inference_windows_batch_size=256,
           start_padding_enabled=False, step_size:int=1,
           scaler_type:str='standard', random_seed:int=1,
           num_workers_loader:int=0, drop_last_loader:bool=False,
           optimizer=None, optimizer_kwargs=None, **trainer_kwargs)

*TimesNet

The TimesNet univariate model tackles the challenge of modeling multiple intraperiod and interperiod temporal variations.*

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TimesNet.fit

 TimesNet.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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TimesNet.predict

 TimesNet.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 numpy as np
import pandas as pd
import pytorch_lightning as pl
import matplotlib.pyplot as plt

from neuralforecast import NeuralForecast
from neuralforecast.losses.pytorch import MQLoss, DistributionLoss
from neuralforecast.utils import AirPassengers, AirPassengersPanel, AirPassengersStatic, augment_calendar_df

AirPassengersPanel, calendar_cols = augment_calendar_df(df=AirPassengersPanel, freq='M')

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=MAE(),
                 #loss=MQLoss(quantiles=[0.2, 0.5, 0.8]),
                 loss=DistributionLoss(distribution='Normal', level=[80, 90]),
                 futr_exog_list=calendar_cols,
                 scaler_type='standard',
                 learning_rate=1e-3,
                 max_steps=5,
                 val_check_steps=50,
                 early_stop_patience_steps=2)

nf = NeuralForecast(
    models=[model],
    freq='M'
)
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()