A Comprehensive Comparative Study of Quick Invariant Signature (QIS), Dynamic Time Warping (DTW), and Hybrid QIS + DTW for Time Series Analysis

Shahbazkia H.R. Khosravani H.R. Pulatov A. Hajimani E. Kiazadeh M.
March 2025 Multidisciplinary Digital Publishing Institute (MDPI)

Mathematics
2025 #13 Issue 6

This study presents a comprehensive evaluation of the quick invariant signature (QIS), dynamic time warping (DTW), and a novel hybrid QIS + DTW approach for time series analysis. QIS, a translation and rotation invariant shape descriptor, and DTW, a widely used alignment technique, were tested individually and in combination across various datasets, including ECG5000, seismic data, and synthetic signals. Our hybrid method was designed to embed the structural representation of the QIS with the temporal alignment capabilities of DTW. This hybrid method achieved a performance of up to 93% classification accuracy on ECG5000, outperforming DTW alone (86%) and a standard MLP classifier in noisy or low-data conditions. These findings confirm that integrating structural invariance (QIS) with temporal alignment (DTW) yields superior robustness to noise and time compression artifacts. We recommend adopting hybrid QIS + DTW, particularly for applications in biomedical signal monitoring and earthquake detection, where real-time analysis and minimal labeled data are critical. The proposed hybrid approach does not require extensive training, making it suitable for resource-constrained scenarios.

affine invariance , hybrid QIS + DTW , noise robustness in time series , structural invariance , time series analysis , time series classification

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KIMEP University, Almaty, 050010, Kazakhstan
DEEI, University of Algarve, Faro, 8005-139, Portugal
SOLTE School, Westminster International University in Tashkent, Tashkent, 100047, Uzbekistan
Alphanumeric Systems Portugal, Nucleo Central–Tagus Park, Room 273, Oeiras, 2740-122, Portugal

KIMEP University
DEEI
SOLTE School
Alphanumeric Systems Portugal

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