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• W3029579534 abstract "Distance measures are core building blocks in time-series analysis and the subject of active research for decades. Unfortunately, the most detailed experimental study in this area is outdated (over a decade old) and, naturally, does not reflect recent progress. Importantly, this study (i) omitted multiple distance measures, including a classic measure in the time-series literature; (ii) considered only a single time-series normalization method; and (iii) reported only raw classification error rates without statistically validating the findings, resulting in or fueling four misconceptions in the time-series literature. Motivated by the aforementioned drawbacks and our curiosity to shed some light on these misconceptions, we comprehensively evaluate 71 time-series distance measures. Specifically, our study includes (i) 8 normalization methods; (ii) 52 lock-step measures; (iii) 4 sliding measures; (iv) 7 elastic measures; (v) 4 kernel functions; and (vi) 4 embedding measures. We extensively evaluate these measures across 128 time-series datasets using rigorous statistical analysis. Our findings debunk four long-standing misconceptions that significantly alter the landscape of what is known about existing distance measures. With the new foundations in place, we discuss open challenges and promising directions." @default.
• W3029579534 created "2020-06-05" @default.
• W3029579534 creator A5016186535 @default.
• W3029579534 creator A5041264166 @default.
• W3029579534 creator A5090643027 @default.
• W3029579534 creator A5091139670 @default.
• W3029579534 date "2020-05-31" @default.
• W3029579534 modified "2023-10-10" @default.
• W3029579534 title "Debunking Four Long-Standing Misconceptions of Time-Series Distance Measures" @default.
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