Model Selection with a Shapelet-based Distance Measure for Multi-source Transfer Learning in Time Series Classification

TL;DR

This paper introduces a novel shapelet-based transferability measure for selecting multiple source datasets to improve time series classification performance with neural networks, reducing computational costs.

Contribution

It proposes a new transferability measure based on shapelet discovery that enables efficient multi-source dataset selection for transfer learning in time series classification.

Findings

Enhanced CNN performance on time series datasets.

Efficient source dataset selection reduces computational costs.

Method applicable across various neural network architectures.

Abstract

Transfer learning is a common practice that alleviates the need for extensive data to train neural networks. It is performed by pre-training a model using a source dataset and fine-tuning it for a target task. However, not every source dataset is appropriate for each target dataset, especially for time series. In this paper, we propose a novel method of selecting and using multiple datasets for transfer learning for time series classification. Specifically, our method combines multiple datasets as one source dataset for pre-training neural networks. Furthermore, for selecting multiple sources, our method measures the transferability of datasets based on shapelet discovery for effective source selection. While traditional transferability measures require considerable time for pre-training all the possible sources for source selection of each possible architecture, our method can be repeatedly used for every possible architecture with a single simple computation. Using the proposed method, we demonstrate that it is possible to increase the performance of temporal convolutional neural networks (CNN) on time series datasets.