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FilterNet: A many-to-many deep learning architecture for time series classification

This repository contains code to reproduce the results and figures in the paper: FilterNet: A many-to-many deep learning architecture for time series classification.

Setup

Easiest way to run this software is via the Anaconda Python distribution.

Running tests

In the root dir of this repo:

pytest tests

Reproducing Results

1. Run the scripts in the scripts/ directory. These are very long-running scripts that reproduce each experimental condition many times. You might want to set, e.g., NUM_REPEATS=1 if you don't need this level of reproducibility.

2. Run the notebooks to re-produce the figures. You might need to edit a few paths to specific models to match the filenames on your system, especially if you changed the NAME or NUM_REPEATS parametesr.

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