Notice of Violation of IEEE Publication Principles - A Local Segmented Dynamic Time Warping Distance Measure Algorithm for Time Series Data Mining

2006 ◽  
Author(s):  
Xiao-li Dong ◽  
Cheng-kui Gu ◽  
Zheng-ou Wang
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Dynamic Time Warping ◽  
Time Series Data ◽  
Distance Measure ◽  
Series Data ◽  
Time Warping ◽  
Time Series Data Mining ◽  
Dynamic Time

scholarly journals Weighted Dynamic Time Warping for Grid-Based Travel-Demand-Pattern Clustering: Case Study of Beijing Bicycle-Sharing System

2019 ◽  
Vol 8 (6) ◽  
pp. 281
Author(s):  
Xiaofei Zhao ◽  
Caiyi Hu ◽  
Zhao Liu ◽  
Yangyang Meng
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Dynamic Time Warping ◽  
Travel Demand ◽  
Time Series Data ◽  
Transportation Systems ◽  
Series Data ◽  
Time Warping ◽  
Dynamic Time ◽  
Grid Based

Many kinds of spatial–temporal data collected by transportation systems, such as user order systems or automated fare-collection (AFC) systems, can be discretized and converted into time-series data. With the technique of time-series data mining, certain travel-demand patterns of different areas in the city can be detected. This study proposes a data-mining model for understanding the patterns and regularities of human activities in urban areas from spatiotemporal datasets. This model uses a grid-based method to convert spatiotemporal point datasets into discretized temporal sequences. Time-series analysis technique dynamic time warping (DTW) is then used to describe the similarity between travel-demand sequences, while the clustering algorithm density-based spatial clustering of applications with noise (DBSCAN), based on modified DTW, is used to detect clusters among the travel-demand samples. Four typical patterns are found, including balanced and unbalanced cases. These findings can help to understand the land-use structure and commuting activities of a city. The results indicate that the grid-based model and time-series analysis model developed in this study can effectively uncover the spatiotemporal characteristics of travel demand from usage data in public transportation systems.

A New Symbolization and Distance Measure Based Anomaly Mining Approach for Hydrological Time Series

2016 ◽  
Vol 13 (3) ◽  
pp. 26-45 ◽  
Author(s):  
Pengcheng Zhang ◽  
Yan Xiao ◽  
Yuelong Zhu ◽  
Jun Feng ◽  
Dingsheng Wan ◽  
...  
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Time Series Data ◽  
Distance Measure ◽  
Series Data ◽  
Feature Points ◽  
Time Warping ◽  
Dynamic Time ◽  
Fitting Error ◽  
Selection Of

Most of the time series data mining tasks attempt to discover data patterns that appear frequently. Abnormal data is often ignored as noise. There are some data mining techniques based on time series to extract anomaly. However, most of these techniques cannot suit big unstable data existing in various fields. Their key problems are high fitting error after dimension reduction and low accuracy of mining results. This paper studies an approach of mining time series abnormal patterns in the hydrological field. The authors propose a new idea to solve the problem of hydrological anomaly mining based on time series. They propose Feature Points Symbolic Aggregate Approximation (FP_SAX) to improve the selection of feature points, and then measures the distance of strings by Symbol Distance based Dynamic Time Warping (SD_DTW). Finally, the distances generated are sorted. A set of dedicated experiments are performed to validate the authors' approach. The results show that their approach has lower fitting error and higher accuracy compared to other approaches.

scholarly journals Incremental fuzzy C medoids clustering of time series data using dynamic time warping distance

PLoS ONE ◽  
2018 ◽  
Vol 13 (5) ◽  
pp. e0197499 ◽  
Author(s):  
Yongli Liu ◽  
Jingli Chen ◽  
Shuai Wu ◽  
Zhizhong Liu ◽  
Hao Chao
Keyword(s):  
Time Series ◽  
Dynamic Time Warping ◽  
Time Series Data ◽  
Series Data ◽  
Time Warping ◽  
Dynamic Time

scholarly journals Optimizing dynamic time warping’s window width for time series data mining applications

2018 ◽  
Vol 32 (4) ◽  
pp. 1074-1120 ◽  
Author(s):  
Hoang Anh Dau ◽  
Diego Furtado Silva ◽  
François Petitjean ◽  
Germain Forestier ◽  
Anthony Bagnall ◽  
...  
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Time Series Data Mining ◽  
Window Width ◽  
Dynamic Time

scholarly journals Free congruence: an exploration of expanded similarity measures for time series data

2021 ◽  
Author(s):  
Lucas Cassiel Jacaruso
Keyword(s):  
Time Series ◽  
Dynamic Time Warping ◽  
Time Series Data ◽  
Similarity Measures ◽  
Distance Measures ◽  
Series Data ◽  
Time Warping ◽  
Point To Point ◽  
Dynamic Time ◽  
Point Distance

Abstract Time series similarity measures are highly relevant in a wide range of emerging applications including training machine learning models, classification, and predictive modeling. Standard similarity measures for time series most often involve point-to-point distance measures including Euclidean distance and Dynamic Time Warping. Such similarity measures fundamentally require the fluctuation of values in the time series being compared to follow a corresponding order or cadence for similarity to be established. Other existing approaches use local statistical tests to detect structural changes in time series. This paper is spurred by the exploration of a broader definition of similarity, namely one that takes into account the sheer numerical resemblance between sets of statistical properties for time series segments irrespectively of value labeling. Further, the presence of common pattern components between time series segments was examined even if they occur in a permuted order, which would not necessarily satisfy the criteria of more conventional point-to-point distance measures. The newly defined similarity measures were tested on time series data representing over 20 years of cooperation intent expressed in global media sentiment. Tests determined whether the newly defined similarity measures would accurately identify stronger resemblance, on average, for pairings of similar time series segments (exhibiting overall decline) than pairings of differing segments (exhibiting overall decline and overall rise). The ability to identify patterns other than the obvious overall rise or decline that can accurately relate samples is regarded as a first step towards assessing the value of the newly explored similarity measures for classification or prediction. Results were compared with those of Dynamic Time Warping on the same data for context. Surprisingly, the test for numerical resemblance between sets of statistical properties established stronger resemblance for pairings of decline years with greater statistical significance than Dynamic Time Warping on the particular data and sample size used.

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