Similarity Measure Based on Incremental Warping Window for Time Series Data Mining
A similarity measure is one of the most important tasks in the fields of time series data mining. Its quality often affects the efficiency and effectiveness of the related algorithms that need to measure the similarity between two time series in advance. Dynamic time warping is one of the most robus...
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doaj-d79babb13b364dc399bb3727c2100e992021-03-29T22:11:25ZengIEEEIEEE Access2169-35362019-01-0173909391710.1109/ACCESS.2018.28897928588323Similarity Measure Based on Incremental Warping Window for Time Series Data MiningHailin Li0https://orcid.org/0000-0001-6924-9689Cheng Wang1College of Business Administration, Huaqiao University, Quanzhou, ChinaCollege of Computer Sciences, Huaqiao University, Xiamen, ChinaA similarity measure is one of the most important tasks in the fields of time series data mining. Its quality often affects the efficiency and effectiveness of the related algorithms that need to measure the similarity between two time series in advance. Dynamic time warping is one of the most robust methods to compare one time series with another based onwarping alignments. In this paper, the design of an incremental warping window is used to improve the performance of dynamic time warping. The incremental warping window is changeable for various time series with different lengths. Moreover, the improved dynamic time warping based on the novel window considers the recent alignments as much as possible, which indicates that the proposed method concentrates on more information of the recent data points than that of the previous data points. In addition, it is suitable for online similarity measure between data stream. The experimental evaluation shows that the proposed method is effective and efficient for time series mining.https://ieeexplore.ieee.org/document/8588323/Dynamic time warpingsimilarity measuretime series data miningincremental warping windowclassification |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Hailin Li Cheng Wang |
spellingShingle |
Hailin Li Cheng Wang Similarity Measure Based on Incremental Warping Window for Time Series Data Mining IEEE Access Dynamic time warping similarity measure time series data mining incremental warping window classification |
author_facet |
Hailin Li Cheng Wang |
author_sort |
Hailin Li |
title |
Similarity Measure Based on Incremental Warping Window for Time Series Data Mining |
title_short |
Similarity Measure Based on Incremental Warping Window for Time Series Data Mining |
title_full |
Similarity Measure Based on Incremental Warping Window for Time Series Data Mining |
title_fullStr |
Similarity Measure Based on Incremental Warping Window for Time Series Data Mining |
title_full_unstemmed |
Similarity Measure Based on Incremental Warping Window for Time Series Data Mining |
title_sort |
similarity measure based on incremental warping window for time series data mining |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2019-01-01 |
description |
A similarity measure is one of the most important tasks in the fields of time series data mining. Its quality often affects the efficiency and effectiveness of the related algorithms that need to measure the similarity between two time series in advance. Dynamic time warping is one of the most robust methods to compare one time series with another based onwarping alignments. In this paper, the design of an incremental warping window is used to improve the performance of dynamic time warping. The incremental warping window is changeable for various time series with different lengths. Moreover, the improved dynamic time warping based on the novel window considers the recent alignments as much as possible, which indicates that the proposed method concentrates on more information of the recent data points than that of the previous data points. In addition, it is suitable for online similarity measure between data stream. The experimental evaluation shows that the proposed method is effective and efficient for time series mining. |
topic |
Dynamic time warping similarity measure time series data mining incremental warping window classification |
url |
https://ieeexplore.ieee.org/document/8588323/ |
work_keys_str_mv |
AT hailinli similaritymeasurebasedonincrementalwarpingwindowfortimeseriesdatamining AT chengwang similaritymeasurebasedonincrementalwarpingwindowfortimeseriesdatamining |
_version_ |
1724192110874198016 |