/pytorch-forecasting

Time series forecasting with PyTorch

Primary LanguagePythonMIT LicenseMIT

Our article on Towards Data Science introduces the package and provides background information.

Pytorch Forecasting aims to ease state-of-the-art timeseries forecasting with neural networks for real-world cases and research alike. The goal is to provide a high-level API with maximum flexibility for professionals and reasonable defaults for beginners. Specifically, the package provides

  • A timeseries dataset class which abstracts handling variable transformations, missing values, randomized subsampling, multiple history lengths, etc.
  • A base model class which provides basic training of timeseries models along with logging in tensorboard and generic visualizations such actual vs predictions and dependency plots
  • Multiple neural network architectures for timeseries forecasting that have been enhanced for real-world deployment and come with in-built interpretation capabilities
  • Multi-horizon timeseries metrics
  • Ranger optimizer for faster model training
  • Hyperparameter tuning with optuna

The package is built on pytorch-lightning to allow training on CPUs, single and multiple GPUs out-of-the-box.

Installation

If you are working on windows, you need to first install PyTorch with

pip install torch -f https://download.pytorch.org/whl/torch_stable.html.

Otherwise, you can proceed with

pip install pytorch-forecasting

Alternatively, you can install the package via conda

conda install pytorch-forecasting pytorch -c pytorch>=1.7 -c conda-forge

PyTorch Forecasting is now installed from the conda-forge channel while PyTorch is install from the pytorch channel.

Documentation

Visit https://pytorch-forecasting.readthedocs.io to read the documentation with detailed tutorials.

Available models

To implement new models, see the How to implement new models tutorial. It covers basic as well as advanced architectures.

Usage

import pytorch_lightning as pl
from pytorch_lightning.callbacks import EarlyStopping, LearningRateMonitor
from pytorch_forecasting.metrics import QuantileLoss
from pytorch_forecasting import TimeSeriesDataSet, TemporalFusionTransformer

# load data
data = ...

# define dataset
max_encoder_length = 36
max_prediction_length = 6
training_cutoff = "YYYY-MM-DD"  # day for cutoff

training = TimeSeriesDataSet(
    data[lambda x: x.date <= training_cutoff],
    time_idx= ...,
    target= ...,
    group_ids=[ ... ],
    max_encoder_length=max_encoder_length,
    max_prediction_length=max_prediction_length,
    static_categoricals=[ ... ],
    static_reals=[ ... ],
    time_varying_known_categoricals=[ ... ],
    time_varying_known_reals=[ ... ],
    time_varying_unknown_categoricals=[ ... ],
    time_varying_unknown_reals=[ ... ],
)


validation = TimeSeriesDataSet.from_dataset(training, data, min_prediction_idx=training.index.time.max() + 1, stop_randomization=True)
batch_size = 128
train_dataloader = training.to_dataloader(train=True, batch_size=batch_size, num_workers=2)
val_dataloader = validation.to_dataloader(train=False, batch_size=batch_size, num_workers=2)


early_stop_callback = EarlyStopping(monitor="val_loss", min_delta=1e-4, patience=1, verbose=False, mode="min")
lr_logger = LearningRateMonitor()
trainer = pl.Trainer(
    max_epochs=100,
    gpus=0,
    gradient_clip_val=0.1,
    limit_train_batches=30,
    callbacks=[lr_logger, early_stop_callback],
)


tft = TemporalFusionTransformer.from_dataset(
    training,
    learning_rate=0.03,
    hidden_size=32,
    attention_head_size=1,
    dropout=0.1,
    hidden_continuous_size=16,
    output_size=7,
    loss=QuantileLoss(),
    log_interval=2,
    reduce_on_plateau_patience=4
)
print(f"Number of parameters in network: {tft.size()/1e3:.1f}k")

# find optimal learning rate
res = trainer.lr_find(
    tft, train_dataloader=train_dataloader, val_dataloaders=val_dataloader, early_stop_threshold=1000.0, max_lr=0.3,
)

print(f"suggested learning rate: {res.suggestion()}")
fig = res.plot(show=True, suggest=True)
fig.show()

trainer.fit(
    tft, train_dataloader=train_dataloader, val_dataloaders=val_dataloader,
)