torch-timeseries 0.0.3a3

Timeseries Learning Library for PyTorch.

pytorch_timeseries

An all in one deep learning library that boost your timeseries research. Check the documentation for more detail.

Compared to previous libraries, pytorch_timeseries is

• dataset automatically downloaded
• easy to use and extend
• clear documentation
• highly customizable
• ..........

installation

pip install torch-timeseries

⚠️⚠️⚠️ Warning: We only support python version >= 3.8+

addtional install

For running Graph Nerual Network based models, pytorch_geometric is also needed.

pip install torch_geometric

# Optional dependencies
pip install pyg_lib torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-2.0.0+cu118.html

Quick Start

1 Forecasting

1.1 download dataset

The dataset will be downloaded automatically!!!!

from torch_timeseries.dataset import ETTh1
from torch_timeseries.dataloader import StandardScaler, SlidingWindow, SlidingWindowTS
from torch_timeseries.model import DLinear
from torch.nn import MSELoss, L1Loss
from torch.optim import Adam
dataset = ETTh1('./data')

1.2 setup scaler/dataloader

Once you setup a dataloader and pass a scaler into this dataloader, the scaler will be fitted on the training set.

scaler = StandardScaler()
dataloader = SlidingWindowTS(dataset, 
                        window=96,
                        horizon=1,
                        steps=336,
                        batch_size=32, 
                        train_ratio=0.7, 
                        val_ratio=0.2, 
                        scaler=scaler,
                        )

After this, you can access the train/val/test loader by dataloader.train_loader/val_loader/test_loader

1.3 training

model = DLinear(dataloader.window, dataloader.steps, dataset.num_features, individual= True)
optimizer = Adam(model.parameters())
loss_function = MSELoss()

# train
model.train()
for scaled_x, scaled_y, x, y, x_date_enc, y_date_enc in dataloader.train_loader:
    optimizer.zero_grad()
    
    scaled_x = scaled_x.float()
    scaled_y = scaled_y.float()
    scaled_pred_y = model(scaled_x) 
    
    loss = loss_function(scaled_pred_y, scaled_y)
    loss.backward()
    optimizer.step()
    print(loss)

1.4 val/test

# val
model.eval()
for scaled_x, scaled_y, x, y, x_date_enc, y_date_enc in dataloader.val_loader:
    ....your validation code here...

# test
model.eval()
for scaled_x, scaled_y, x, y, x_date_enc, y_date_enc in dataloader.test_loader:
    ....your test code here...

2 Imputation

1. download dataset

The dataset will be downloaded automatically!!!!

from torch_timeseries.dataset import ETTh1
from torch_timeseries.dataloader import StandardScaler, SlidingWindow, SlidingWindowTS
from torch_timeseries.model import DLinear
from torch.nn import MSELoss, L1Loss
from torch.optim import Adam
dataset = ETTh1('./data')

2. setup scaler/dataloader

Once you setup a dataloader and pass a scaler into this dataloader, the scaler will be fitted on the training set.

scaler = StandardScaler()
dataloader = SlidingWindowTS(dataset, 
                        window=96,
                        horizon=1,
                        steps=336,
                        batch_size=32, 
                        train_ratio=0.7, 
                        val_ratio=0.2, 
                        scaler=scaler,
                        )

After this, you can access the train/val/test loader by dataloader.train_loader/val_loader/test_loader

3. training

model = DLinear(dataloader.window, dataloader.steps, dataset.num_features, individual= True)
optimizer = Adam(model.parameters())
loss_function = MSELoss()

# train
model.train()
for scaled_x, scaled_y, x, y, x_date_enc, y_date_enc in dataloader.train_loader:
    optimizer.zero_grad()
    
    scaled_x = scaled_x.float()
    scaled_y = scaled_y.float()
    scaled_pred_y = model(scaled_x) 
    
    loss = loss_function(scaled_pred_y, scaled_y)
    loss.backward()
    optimizer.step()
    print(loss)

4. val/test

# val
model.eval()
for scaled_x, scaled_y, x, y, x_date_enc, y_date_enc in dataloader.val_loader:
    scaled_x = scaled_x.float()
    scaled_y = scaled_y.float()
    scaled_pred_y = model(scaled_x) 
    loss = loss_function(scaled_pred_y, scaled_y)
    

# test
model.eval()
for scaled_x, scaled_y, x, y, x_date_enc, y_date_enc in dataloader.test_loader:
    scaled_x = scaled_x.float()
    scaled_y = scaled_y.float()
    scaled_pred_y = model(scaled_x) 
    loss = loss_function(scaled_pred_y, scaled_y)
    

dev install

install requirements

Note:This library assumes that you've installed Pytorch according to it's official website, the basic dependencies of torch > > related libraries may not be listed in the requirements files: https://pytorch.org/get-started/locally/

The recommended python version is 3.8.1+. Please first install torch according to your environment.

pip3 install torch torchvision torchaudio