Channel-Adaptive Early Exiting using Reinforcement Learning for Multivariate Time Series Classification
This repository contains the official implementation of the CHARLEE framework described in Channel-Adaptive Early Exiting using Reinforcement Learning for Multivariate Time Series Classification, accepted at ICMLA 2023. The extended arXiv version has a different title and abstract for double-blind review purposes.
The code is written in Python 3.8.13 and uses the following main dependencies:
- torch==1.13.0
- tsai==0.3.4
- sktime==0.13.4
- numpy==1.21.5
- scikit-learn==1.1.3
- pandas==1.5.1
This repository contains code from the repository of Dhariyal et al., utilizing their work on channel selection for multivariate time series classification.
Moreover, the initial structure of the framework and files is based on the code for Stop&Hop, an early classification method for irregular time series, by Hartvigsen et al.
The models are evaluated on a subset of the UEA multivariate dataset collection. Moreover, we utilized datasets based on the MAFAULDA Machinery Fault Database and the Case Western Reserve University Bearing Data.
The synthetic dataset used is included in the data directory.