Documentation Index
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NLinear is a simple and fast yet accurate time series forecasting model
for long-horizon forecasting.
The architecture aims to boost the performance when there is a
distribution shift in the dataset:
- NLinear first subtracts the input
by the last value of the sequence;
- Then, the input goes through a
linear layer, and the subtracted part is added back before making the
final prediction.
References
Figure 1. DLinear
Architecture.
NLinear
NLinear
NLinear(
h,
input_size,
stat_exog_list=None,
hist_exog_list=None,
futr_exog_list=None,
exclude_insample_y=False,
loss=MAE(),
valid_loss=None,
max_steps=5000,
learning_rate=0.0001,
num_lr_decays=-1,
early_stop_patience_steps=-1,
val_monitor="ptl/val_loss",
val_check_steps=100,
batch_size=32,
valid_batch_size=None,
windows_batch_size=1024,
inference_windows_batch_size=1024,
start_padding_enabled=False,
training_data_availability_threshold=0.0,
step_size=1,
scaler_type="identity",
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
)
BaseModel
NLinear
Parameters:
| Name | Type | Description | Default |
|---|
h | int | forecast horizon. | required |
input_size | int | maximum sequence length for truncated train backpropagation. | required |
stat_exog_list | str list | static exogenous columns. | None |
hist_exog_list | str list | historic exogenous columns. | None |
futr_exog_list | str list | future exogenous columns. | None |
exclude_insample_y | bool | the model skips the autoregressive features y[t-input_size:t] if True. | False |
loss | PyTorch module | instantiated train loss class from losses collection. | MAE() |
valid_loss | PyTorch module | instantiated valid loss class from losses collection. | None |
max_steps | int | maximum number of training steps. | 5000 |
learning_rate | float | Learning rate between (0, 1). | 0.0001 |
num_lr_decays | int | Number of learning rate decays, evenly distributed across max_steps. | -1 |
early_stop_patience_steps | int | Number of validation iterations before early stopping. | -1 |
val_monitor | str | metric to monitor for early stopping. Valid options: “ptl/val_loss”, “valid_loss”, “train_loss”. Default: “ptl/val_loss”. | ‘ptl/val_loss’ |
val_check_steps | int | Number of training steps between every validation loss check. | 100 |
batch_size | int | number of different series in each batch. | 32 |
valid_batch_size | int | number of different series in each validation and test batch, if None uses batch_size. | None |
windows_batch_size | int | number of windows to sample in each training batch, default uses all. | 1024 |
inference_windows_batch_size | int | number of windows to sample in each inference batch. | 1024 |
start_padding_enabled | bool | if True, the model will pad the time series with zeros at the beginning, by input size. | False |
training_data_availability_threshold | Union[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_size | int | step size between each window of temporal data. | 1 |
scaler_type | str | type of scaler for temporal inputs normalization see temporal scalers. | ‘identity’ |
random_seed | int | random_seed for pytorch initializer and numpy generators. | 1 |
drop_last_loader | bool | if True TimeSeriesDataLoader drops last non-full batch. | False |
alias | str | optional, Custom name of the model. | None |
optimizer | Subclass of ‘torch.optim.Optimizer’ | optional, user specified optimizer instead of the default choice (Adam). | None |
optimizer_kwargs | dict | optional, list of parameters used by the user specified optimizer. | None |
lr_scheduler | Subclass of ‘torch.optim.lr_scheduler.LRScheduler’ | optional, user specified lr_scheduler instead of the default choice (StepLR). | None |
lr_scheduler_kwargs | dict | optional, list of parameters used by the user specified lr_scheduler. | None |
dataloader_kwargs | dict | optional, list of parameters passed into the PyTorch Lightning dataloader by the TimeSeriesDataLoader. | None |
**trainer_kwargs | int | keyword trainer arguments inherited from PyTorch Lighning’s trainer. | |
NLinear.fit
fit(
dataset, val_size=0, test_size=0, random_seed=None, distributed_config=None
)
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:
| Name | Type | Description | Default |
|---|
dataset | TimeSeriesDataset | NeuralForecast’s TimeSeriesDataset, see documentation. | required |
val_size | int | Validation size for temporal cross-validation. | 0 |
random_seed | int | Random seed for pytorch initializer and numpy generators, overwrites model.init’s. | None |
test_size | int | Test size for temporal cross-validation. | 0 |
NLinear.predict
predict(
dataset,
test_size=None,
step_size=1,
random_seed=None,
quantiles=None,
h=None,
explainer_config=None,
**data_module_kwargs
)
Trainer execution of predict_step.
Parameters:
| Name | Type | Description | Default |
|---|
dataset | TimeSeriesDataset | NeuralForecast’s TimeSeriesDataset, see documentation. | required |
test_size | int | Test size for temporal cross-validation. | None |
step_size | int | Step size between each window. | 1 |
random_seed | int | Random seed for pytorch initializer and numpy generators, overwrites model.init’s. | None |
quantiles | list | Target quantiles to predict. | None |
h | int | Prediction horizon, if None, uses the model’s fitted horizon. Defaults to None. | None |
explainer_config | dict | configuration for explanations. | None |
**data_module_kwargs | dict | PL’s TimeSeriesDataModule args, see documentation. | |
Usage Example
import pandas as pd
import matplotlib.pyplot as plt
from neuralforecast import NeuralForecast
from neuralforecast.models import NLinear
from neuralforecast.losses.pytorch import 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 train
Y_test_df = AirPassengersPanel[AirPassengersPanel.ds>=AirPassengersPanel['ds'].values[-12]].reset_index(drop=True) # 12 test
model = NLinear(h=12,
input_size=24,
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='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['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()
