This package is a demonstration of how to train and use TSMixer for forecasting.
pip install -e .[all]
Alternatively, you don't need to include any optional dependencies (i.e. pip install -e .
)
To download the datasets either:
make datasets
To train the models as per the paper run:
make train-weather [out_dir="."]
make train-ETTm2 [out_dir="."]
make train-electricity [out_dir="."]
make train-traffic [out_dir="."]
To train the model with custom parameters, refer to the help via:
python3 -m tsmixer train --help
For the following example, we train on the weather dataset. Our losses and errors are as follow:
Forecasts were run using:
make forecast out_dir="./weather"