@inproceedings{71f96ea58cfb42a783df6f2e7c265f48,
title = "Classification of Univariate Time Series via Temporal Abstraction and Deep Learning",
abstract = "Many time series classification algorithms have been proposed, including deep neural networks based, which so far focused mainly on improving model architectures rather than on data pre-processing. Generalization is crucial in time series classification and it can be achieved by abstracting the data. Data abstraction may also be useful to avoid handling challenges with error measurements, missing values, and irregular sampling. We propose transforming the raw time series into a symbolic time series representation, using a method known as temporal abstraction, before feeding it to the deep neural networks. This transformation can greatly enhance generalization and may potentially improve classification performance. In particular, we investigate the effectiveness of temporal abstraction when combined with convolution-based sequence models or recurrent neural networks. The methods were evaluated on 128 univariate datasets. Our evaluation shows that even when using equal frequency discretization, a relatively simple method, outperforms most state-of-the-art deep neural networks' performance for univariate time series classification when fed by raw time series.",
keywords = "classification, temporal abstraction, time series",
author = "Nevo Itzhak and Shahar Tal and Hadas Cohen and Osher Daniel and Roze Kopylov and Robert Moskovitch",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Conference on Big Data, Big Data 2022 ; Conference date: 17-12-2022 Through 20-12-2022",
year = "2022",
month = jan,
day = "1",
doi = "10.1109/BigData55660.2022.10020752",
language = "English",
series = "Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022",
publisher = "Institute of Electrical and Electronics Engineers",
pages = "1260--1265",
editor = "Shusaku Tsumoto and Yukio Ohsawa and Lei Chen and {Van den Poel}, Dirk and Xiaohua Hu and Yoichi Motomura and Takuya Takagi and Lingfei Wu and Ying Xie and Akihiro Abe and Vijay Raghavan",
booktitle = "Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022",
address = "United States",
}