Our article on Towards Data Science introduces the package and provides background information.
Pytorch Forecasting aims to ease timeseries forecasting with neural networks for real-world cases and research alike. 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.
If you are working 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 -c conda-forge
If you do not have pytorch installed, install it is recommended to install it first from the pytorch channel
conda install pytorch -c pytorch
Visit https://pytorch-forecasting.readthedocs.io to read the documentation with detailed tutorials.
- Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting which outperforms DeepAR by Amazon by 36-69% in benchmarks
- N-BEATS: Neural basis expansion analysis for interpretable time series forecasting which has (if used as ensemble) outperformed all other methods including ensembles of traditional statical methods in the M4 competition. The M4 competition is arguably the most important benchmark for univariate time series forecasting.
import pytorch_lightning as pl
from pytorch_lightning.callbacks import EarlyStopping
from pytorch_forecasting import TimeSeriesDataSet, TemporalFusionTransformer
# load data
data = ...
# define dataset
max_encode_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_encode_length=max_encode_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 = LearningRateLogger()
trainer = pl.Trainer(
max_epochs=100,
gpus=0,
gradient_clip_val=0.1,
early_stop_callback=early_stop_callback,
limit_train_batches=30,
callbacks=[lr_logger],
)
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,
)