Model Selection with a Shapelet-based Distance Measure for Multi-source Transfer Learning in Time Series Classification
This repository is to introduce our paper "Model Selection with a Shapelet-based Distance Measure for Multi-source Transfer Learning in Time Series Classification", accepted at ICPR 2024 (International Conference on Pattern Recognition 2024) and also available in arXiv.
cd docker
docker build -t transferlearn .
This repository uses the UCR Time Series Archive 2018 datasets. To install the datasets, download the .zip file from hear, and extract the contents into the "data/".
The codes are divided into three parts: Shapelet Discovery, Dataset Ranking with Shapelet-based Similarity, and Multi-source Transfer Learning.
We discover shapelets with matrix profile based approach (Yeh et al., 2016).
python generate_shapelet.py
The codes are modified from the following link
- Yeh, Chin-Chia Michael, et al. "Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets." 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 2016.
This is to calculate our novel method of Shapelet-based Similarity for transferability estimation.
python calculate_shapelet_distance.py
This does multi-source tranfer learning with the rankings of shapelet-based similarity. The following line conducts multi-source transfer learning with 10 sources selected by Minimum Shapelet for all the 128 UCR Archive datasets.
python main.py -dn 10 --target experiment --metric Minimum_Shapelet
If you use this repository, please cite us as follows:
@inproceedings{lee2024model,
title={Model Selection with a Shapelet-Based Distance Measure for Multi-source Transfer Learning in Time Series Classification},
author={Lee, Jiseok and Iwana, Brian Kenji},
booktitle={International Conference on Pattern Recognition},
pages={160--175},
year={2024},
organization={Springer}
}