Abstract
Several Python libraries have been released for training time series forecasting models in the last few years. Most include classical statistical approaches, machine learning models, and recent deep learning architectures. Despite the great work for releasing such open-source resources, a tool that allows testing Deep Learning architectures in a framework that guarantees transparent input output management, reproducibility of the results, and expandability of the supported models is still lacking. With DSIPTS, we fill this gap, providing the community with a tool for training and comparing Deep Learning models in the time series forecasting field.
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