Temporal fusion transformer for timeseries forecasting
Project description
Timeseries forecasting with Pytorch
Install with
pip install pytorch-forecasting
Available models
Usage
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= ...,
# weight="weight",
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)
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")
trainer = pl.Trainer(
max_epochs=10,
gpus=0,
gradient_clip_val=0.1,
early_stop_callback=early_stop_callback,
)
tft = TemporalFusionTransformer.from_dataset(training)
print(f"Number of parameters in network: {tft.size()/1e3:.1f}k")
trainer.fit(
tft, train_dataloader=train_dataloader, val_dataloaders=val_dataloader,
)
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