@inproceedings{92fea7738d794032979289bd24f224d1,
title = "Transfer Learning for Time Series Classification Using Synthetic Data Generation",
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.",
keywords = "Synthetic data, Time series classification, Transfer learning",
author = "Yarden Rotem and Nathaniel Shimoni and Lior Rokach and Bracha Shapira",
note = "Publisher Copyright: {\textcopyright} 2022, Springer Nature Switzerland AG.; 6th International Symposium on Cyber Security Cryptography and Machine Learning, CSCML 2022 ; Conference date: 30-06-2022 Through 01-07-2022",
year = "2022",
month = jan,
day = "1",
doi = "10.1007/978-3-031-07689-3_18",
language = "American English",
isbn = "9783031076886",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "232--246",
editor = "Shlomi Dolev and Amnon Meisels and Jonathan Katz",
booktitle = "Cyber Security, Cryptology, and Machine Learning - 6th International Symposium, CSCML 2022, Proceedings",
address = "Germany",
}