Transfer learning for time series classification using synthetic data generation

2022/07/16 by Yarden Rotem, Nathaniel Shimoni, Rotem, Yarden +5
Computer Science · Engineering · #Advanced Chemical Sensor Technologies #Anomaly Detection Techniques and Applications #FOS: Computer and information sciences #Machine Learning (cs.LG) #Time Series Analysis and Forecasting

paper · pdf · doi:10.48550/arxiv.2207.07897

openalex publication_date 2022/07/16 · openalex created_date 2022/07/21 · openalex updated_date 2026/07/28

Abstract

In this paper, we propose an innovative Transfer learning for Time series classification method. Instead of using an existing dataset from the UCR archive as the source dataset, we generated a 15,000,000 synthetic univariate time series dataset that was created using our unique synthetic time series generator algorithm which can generate data with diverse patterns and angles and different sequence lengths. Furthermore, instead of using classification tasks provided by the UCR archive as the source task as previous studies did,we used our own 55 regression tasks as the source tasks, which produced better results than selecting classification tasks from the UCR archive

Related

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• Model Selection with a Shapelet-based Distance Measure for Multi-source Transfer Learning in Time Series Classification
• The Great Time Series Classification Bake Off: An Experimental Evaluation of Recently Proposed Algorithms. Extended Version
• Improving time series estimation and prediction via transfer learning
• The UEA multivariate time series classification archive, 2018