# AUTOGENERATED! DO NOT EDIT! File to edit: ../../nbs/models.dlinear.ipynb.
# %% auto 0
__all__ = ['MovingAvg', 'SeriesDecomp', 'DLinear']
# %% ../../nbs/models.dlinear.ipynb 5
from typing import Optional
import torch
import torch.nn as nn
from ..common._base_windows import BaseWindows
from ..losses.pytorch import MAE
# %% ../../nbs/models.dlinear.ipynb 8
class MovingAvg(nn.Module):
"""
Moving average block to highlight the trend of time series
"""
def __init__(self, kernel_size, stride):
super(MovingAvg, self).__init__()
self.kernel_size = kernel_size
self.avg = nn.AvgPool1d(kernel_size=kernel_size, stride=stride, padding=0)
def forward(self, x):
# padding on the both ends of time series
front = x[:, 0:1].repeat(1, (self.kernel_size - 1) // 2)
end = x[:, -1:].repeat(1, (self.kernel_size - 1) // 2)
x = torch.cat([front, x, end], dim=1)
x = self.avg(x)
return x
class SeriesDecomp(nn.Module):
"""
Series decomposition block
"""
def __init__(self, kernel_size):
super(SeriesDecomp, self).__init__()
self.MovingAvg = MovingAvg(kernel_size, stride=1)
def forward(self, x):
moving_mean = self.MovingAvg(x)
res = x - moving_mean
return res, moving_mean
# %% ../../nbs/models.dlinear.ipynb 10
class DLinear(BaseWindows):
"""DLinear
*Parameters:*
`h`: int, forecast horizon.
`input_size`: int, maximum sequence length for truncated train backpropagation. Default -1 uses all history.
`futr_exog_list`: str list, future exogenous columns.
`hist_exog_list`: str list, historic exogenous columns.
`stat_exog_list`: str list, static exogenous columns.
`exclude_insample_y`: bool=False, the model skips the autoregressive features y[t-input_size:t] if True.
`moving_avg_window`: int=25, window size for trend-seasonality decomposition. Should be uneven.
`loss`: PyTorch module, instantiated train loss class from [losses collection](https://nixtla.github.io/neuralforecast/losses.pytorch.html).
`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=1024, number of windows to sample in each inference batch.
`start_padding_enabled`: bool=False, if True, the model will pad the time series with zeros at the beginning, by input size.
`scaler_type`: str='robust', type of scaler for temporal inputs normalization see [temporal scalers](https://nixtla.github.io/neuralforecast/common.scalers.html).
`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`.
`**trainer_kwargs`: int, keyword trainer arguments inherited from [PyTorch Lighning's trainer](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer).
*References*
- Zeng, Ailing, et al. "Are transformers effective for time series forecasting?." Proceedings of the AAAI conference on artificial intelligence. Vol. 37. No. 9. 2023."
"""
# Class attributes
SAMPLING_TYPE = "windows"
def __init__(
self,
h: int,
input_size: int,
stat_exog_list=None,
hist_exog_list=None,
futr_exog_list=None,
exclude_insample_y=False,
moving_avg_window: int = 25,
loss=MAE(),
valid_loss=None,
max_steps: int = 5000,
learning_rate: float = 1e-4,
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=1024,
inference_windows_batch_size=1024,
start_padding_enabled=False,
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,
**trainer_kwargs
):
super(DLinear, self).__init__(
h=h,
input_size=input_size,
hist_exog_list=hist_exog_list,
stat_exog_list=stat_exog_list,
futr_exog_list=futr_exog_list,
exclude_insample_y=exclude_insample_y,
loss=loss,
valid_loss=valid_loss,
max_steps=max_steps,
learning_rate=learning_rate,
num_lr_decays=num_lr_decays,
early_stop_patience_steps=early_stop_patience_steps,
val_check_steps=val_check_steps,
batch_size=batch_size,
windows_batch_size=windows_batch_size,
valid_batch_size=valid_batch_size,
inference_windows_batch_size=inference_windows_batch_size,
start_padding_enabled=start_padding_enabled,
step_size=step_size,
scaler_type=scaler_type,
num_workers_loader=num_workers_loader,
drop_last_loader=drop_last_loader,
random_seed=random_seed,
optimizer=optimizer,
optimizer_kwargs=optimizer_kwargs,
**trainer_kwargs
)
# Architecture
self.futr_input_size = len(self.futr_exog_list)
self.hist_input_size = len(self.hist_exog_list)
self.stat_input_size = len(self.stat_exog_list)
if self.stat_input_size > 0:
raise Exception("DLinear does not support static variables yet")
if self.hist_input_size > 0:
raise Exception("DLinear does not support historical variables yet")
if self.futr_input_size > 0:
raise Exception("DLinear does not support future variables yet")
if moving_avg_window % 2 == 0:
raise Exception("moving_avg_window should be uneven")
self.c_out = self.loss.outputsize_multiplier
self.output_attention = False
self.enc_in = 1
self.dec_in = 1
# Decomposition
self.decomp = SeriesDecomp(moving_avg_window)
self.linear_trend = nn.Linear(
self.input_size, self.loss.outputsize_multiplier * h, bias=True
)
self.linear_season = nn.Linear(
self.input_size, self.loss.outputsize_multiplier * h, bias=True
)
def forward(self, windows_batch):
# Parse windows_batch
insample_y = windows_batch["insample_y"]
# insample_mask = windows_batch['insample_mask']
# hist_exog = windows_batch['hist_exog']
# stat_exog = windows_batch['stat_exog']
# futr_exog = windows_batch['futr_exog']
# Parse inputs
batch_size = len(insample_y)
seasonal_init, trend_init = self.decomp(insample_y)
trend_part = self.linear_trend(trend_init)
seasonal_part = self.linear_season(seasonal_init)
# Final
forecast = trend_part + seasonal_part
forecast = forecast.reshape(batch_size, self.h, self.loss.outputsize_multiplier)
forecast = self.loss.domain_map(forecast)
return forecast