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Lagged encoding for image‐based time series classification using convolutional neural networks
Author(s) -
Jastrzebska Agnieszka
Publication year - 2020
Publication title -
statistical analysis and data mining: the asa data science journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.381
H-Index - 33
eISSN - 1932-1872
pISSN - 1932-1864
DOI - 10.1002/sam.11455
Subject(s) - computer science , series (stratigraphy) , convolutional neural network , encoding (memory) , artificial intelligence , time series , pattern recognition (psychology) , image (mathematics) , representation (politics) , machine learning , contextual image classification , artificial neural network , data mining , paleontology , politics , political science , law , biology
Time series classification is a thriving area of research in machine learning. Among many applications, it is frequently applied to human activity analysis. Time series describing a human in motion are ubiquitously collected via omnipresent mobile devices and can be subjected to further processing. In this paper, we propose a novel, deep learning approach to time series classification. It is based on a lagged time series representation stored as images and Convolutional Neural Network used to image classification. We present a comparative study on different variants of lagged time series representation and we evaluate their effectiveness in a series of empirical experiments. We show that the developed method provides satisfying classification accuracy. The proposed image‐based time series encoding is less resource‐consuming than encodings used in other image‐based approaches to time series classification. It is worth to emphasize that the proposed time series encoding conceals original time series values. Images are saved without scales and the order of observations cannot be reconstructed. Thus, the method is particularly suitable for systems that need to store sensitive information.