Forecasting timeseries with PyTorch - dataloaders, normalizers, metrics and models
Project description
Pytorch Forecasting aims to ease timeseries forecasting with neural networks. It specificially provides a class to wrap timeseries datasets and a number of PyTorch models.
Installation
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
Visit the documentation at https://pytorch-forecasting.readthedocs.io.
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= ...,
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,
)
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