AccScience Publishing / IJOSI / Volume 9 / Issue 2 / DOI: 10.6977/IJoSI.202504_9(2).0006
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Measuring the accuracy of time series reduction methods based on modified dynamic time warping distance calculations

Anupama Jawale1* ,  Amiya Kumar Tripathy2
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1 Department of Information Technology, Narsee Monjee College of Commerce and Economics, Mumbai, Maharashtra, India
2 Department of Computer Engineering, Don Bosco Institute of Technology, Mumbai, Maharashtra, India
IJOSI 2025 , 9(2), 60–73; https://doi.org/10.6977/IJoSI.202504_9(2).0006
Submitted: 11 September 2024 | Revised: 22 September 2024 | Accepted: 25 November 2024 | Published: 8 April 2025
© 2025 by the Author(s). Licensee AccScience Publishing, USA. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC BY-NC 4.0) ( https://creativecommons.org/licenses/by-nc/4.0/ )
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Abstract

Representation of sensor data in the form of time series is a crucial aspect of numerous related tasks such as comparison, reduction, clustering, and classification. Time series representation methods included in most programming languages/integrated development environments support dimensionality reduction, data preprocessing, and feature extraction for time series data, as do several normalization techniques. This research study focused on 14 different methods of dimensionality reduction from the TSepr (R Studio) package on eight different time series, which are collections of sensor data of varying lengths. The similarity of the reduced time series and the original time series is compared using a modified version of dynamic time warping with time alignment measurement. These methods are further combined with the Gaussian kernel function to normalize the distance between variously aligned series. The results showed that perceptually important points (PIP) and piecewise linear approximation (PLA) were found as the best methods for TS reduction with a minimum deviation (error term) as low as 5 – 12%. The results also indicate that PIP performs significantly differently compared to seasonal decomposition, while there are no significant differences between PIP and the other methods (PLA, FEACLIPTREND, and FEACLIP). In addition, this research study demonstrated the development of an interactive web-based application in which time series are stored in csv files, and the distance between them is calculated through the chosen reduction method.

Keywords
Dimensionality
Distance
Dynamic Time Warping
Gaussian Kernel
Time Series
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International Journal of Systematic Innovation, Electronic ISSN: 2077-8767 Print ISSN: 2077-7973, Published by AccScience Publishing
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