from typing import Optional
import torch.nn as nn
from neuralforecast.common._base_windows import BaseWindows
from neuralforecast.losses.pytorch import MAE非线性
NLinear是一个简单、快速且准确的长期预测时间序列模型。
该架构旨在提高数据集在分布变化时的性能： 1. NLinear首先将输入减去序列的最后一个值； 2. 然后，输入经过一个线性层处理，减去的部分在做出最终预测之前再加回来。
参考文献
- 曾爱玲等。“变压器在时间序列预测中有效吗？。”人工智能会议的会议录。第37卷，第9期，2023年。”
from fastcore.test import test_eq
from nbdev.showdoc import show_docclass NLinear(BaseWindows):
""" NLinear
*Parameters:*<br>
`h`: int, forecast horizon.<br>
`input_size`: int, maximum sequence length for truncated train backpropagation. Default -1 uses all history.<br>
`futr_exog_list`: str list, future exogenous columns.<br>
`hist_exog_list`: str list, historic exogenous columns.<br>
`stat_exog_list`: str list, static exogenous columns.<br>
`exclude_insample_y`: bool=False, the model skips the autoregressive features y[t-input_size:t] if True.<br>
`loss`: PyTorch module, instantiated train loss class from [losses collection](https://nixtla.github.io/neuralforecast/losses.pytorch.html).<br>
`max_steps`: int=1000, maximum number of training steps.<br>
`learning_rate`: float=1e-3, Learning rate between (0, 1).<br>
`num_lr_decays`: int=-1, Number of learning rate decays, evenly distributed across max_steps.<br>
`early_stop_patience_steps`: int=-1, Number of validation iterations before early stopping.<br>
`val_check_steps`: int=100, Number of training steps between every validation loss check.<br>
`batch_size`: int=32, number of different series in each batch.<br>
`valid_batch_size`: int=None, number of different series in each validation and test batch, if None uses batch_size.<br>
`windows_batch_size`: int=1024, number of windows to sample in each training batch, default uses all.<br>
`inference_windows_batch_size`: int=1024, number of windows to sample in each inference batch.<br>
`start_padding_enabled`: bool=False, if True, the model will pad the time series with zeros at the beginning, by input size.<br>
`scaler_type`: str='robust', type of scaler for temporal inputs normalization see [temporal scalers](https://nixtla.github.io/neuralforecast/common.scalers.html).<br>
`random_seed`: int=1, random_seed for pytorch initializer and numpy generators.<br>
`num_workers_loader`: int=os.cpu_count(), workers to be used by `TimeSeriesDataLoader`.<br>
`drop_last_loader`: bool=False, if True `TimeSeriesDataLoader` drops last non-full batch.<br>
`alias`: str, optional, Custom name of the model.<br>
`optimizer`: Subclass of 'torch.optim.Optimizer', optional, user specified optimizer instead of the default choice (Adam).<br>
`optimizer_kwargs`: dict, optional, list of parameters used by the user specified `optimizer`.<br>
`lr_scheduler`: Subclass of 'torch.optim.lr_scheduler.LRScheduler', optional, user specified lr_scheduler instead of the default choice (StepLR).<br>
`lr_scheduler_kwargs`: dict, optional, list of parameters used by the user specified `lr_scheduler`.<br>
`**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).<br>
*References*<br>
- 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'
EXOGENOUS_FUTR = False
EXOGENOUS_HIST = False
EXOGENOUS_STAT = False
def __init__(self,
h: int,
input_size: int,
stat_exog_list = None,
hist_exog_list = None,
futr_exog_list = None,
exclude_insample_y = False,
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,
lr_scheduler = None,
lr_scheduler_kwargs = None,
**trainer_kwargs):
super(NLinear, 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,
lr_scheduler=lr_scheduler,
lr_scheduler_kwargs=lr_scheduler_kwargs,
**trainer_kwargs)
# Architecture
self.c_out = self.loss.outputsize_multiplier
self.output_attention = False
self.enc_in = 1
self.dec_in = 1
self.linear = 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']
# 解析输入
batch_size = len(insample_y)
# 输入归一化
last_value = insample_y[:, -1:]
norm_insample_y = insample_y - last_value
# 最终
forecast = self.linear(norm_insample_y) + last_value
forecast = forecast.reshape(batch_size, self.h, self.loss.outputsize_multiplier)
forecast = self.loss.domain_map(forecast)
return forecastshow_doc(NLinear)show_doc(NLinear.fit, name='NLinear.fit')show_doc(NLinear.predict, name='NLinear.predict')使用示例
import pandas as pd
import matplotlib.pyplot as plt
from neuralforecast import NeuralForecast
from neuralforecast.models import NLinear
from neuralforecast.losses.pytorch import MQLoss, DistributionLoss
from neuralforecast.utils import 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次列车
Y_test_df = AirPassengersPanel[AirPassengersPanel.ds>=AirPassengersPanel['ds'].values[-12]].reset_index(drop=True) # 12项测试
model = NLinear(h=12,
input_size=24,
loss=MAE(),
#loss=DistributionLoss(distribution='StudentT', level=[80, 90], return_params=True),
scaler_type='robust',
learning_rate=1e-3,
max_steps=500,
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['NLinear-median'], c='blue', label='median')
plt.fill_between(x=plot_df['ds'][-12:],
y1=plot_df['NLinear-lo-90'][-12:].values,
y2=plot_df['NLinear-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['NLinear'], c='blue', label='Forecast')
plt.legend()
plt.grid()Give us a ⭐ on Github