hfawaz/dl-4-tsc

This is the companion repository for our paper titled "Deep learning for time series classification: a review" published in Data Mining and Knowledge Discovery, also available on ArXiv. All python packages needed are listed in pip-requirements.txt Our results showed that a deep residual network architecture performs best for the time series classification task. The following table contains the averaged accuracy over 10 runs of each implemented model on the UCR/UEA archive, with the standard deviation between parentheses. The following table contains the averaged accuracy over 10 runs of each implemented model on the MTS archive, with the standard deviation between parentheses.