Evaluating DTW Measures via a Synthesis Framework for Time-Series Data

TL;DR

This paper introduces a synthesis framework to evaluate various DTW measures for time-series comparison, providing guidelines for selecting the appropriate measure based on data variations, validated through real-world applications.

Contribution

It presents a novel synthesis framework for modeling time-series variations and systematically evaluates DTW measures, offering practical guidelines for their selection.

Findings

Different DTW measures perform variably depending on data variations.

The framework enables controlled assessment of DTW performance.

Guidelines for choosing DTW measures improve time-series analysis accuracy.

Abstract

Time-series data originate from various applications that describe specific observations or quantities of interest over time. Their analysis often involves the comparison across different time-series data sequences, which in turn requires the alignment of these sequences. Dynamic Time Warping (DTW) is the standard approach to achieve an optimal alignment between two temporal signals. Different variations of DTW have been proposed to address various needs for signal alignment or classifications. However, a comprehensive evaluation of their performance in these time-series data processing tasks is lacking. Most DTW measures perform well on certain types of time-series data without a clear explanation of the reason. To address that, we propose a synthesis framework to model the variation between two time-series data sequences for comparison. Our synthesis framework can produce a realistic initial signal and deform it with controllable variations that mimic real-world scenarios. With this synthesis framework, we produce a large number of time-series sequence pairs with different but known variations, which are used to assess the performance of a number of well-known DTW measures for the tasks of alignment and classification. We report their performance on different variations and suggest the proper DTW measure to use based on the type of variations between two time-series sequences. This is the first time such a guideline is presented for selecting a proper DTW measure. To validate our conclusion, we apply our findings to real-world applications, i.e., the detection of the formation top for the oil and gas industry and the pattern search in streamlines for flow visualization.