scholarly journals Indexable online time series segmentation with error bound guarantee

2013 ◽  
Vol 18 (2) ◽  
pp. 359-401 ◽  
Author(s):  
Jianzhong Qi ◽  
Rui Zhang ◽  
Kotagiri Ramamohanarao ◽  
Hongzhi Wang ◽  
Zeyi Wen ◽  
...  
Keyword(s):  
Time Series ◽  
Error Bound ◽  
Time Series Segmentation

A new approach for optimal offline time-series segmentation with error bound guarantee

2021 ◽  
Vol 115 ◽  
pp. 107917
Author(s):  
Ángel Carmona-Poyato ◽  
Nicolás Luis Fernández-Garcia ◽  
Francisco José Madrid-Cuevas ◽  
Antonio Manuel Durán-Rosal
Keyword(s):  
Time Series ◽  
Error Bound ◽  
New Approach ◽  
Time Series Segmentation

scholarly journals A Novel Time-Sensitive Composite Similarity Model for Multivariate Time-Series Correlation Analysis

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 731
Author(s):  
Mengxia Liang ◽  
Xiaolong Wang ◽  
Shaocong Wu
Keyword(s):  
Time Series ◽  
Correlation Analysis ◽  
Stock Price ◽  
Multivariate Time Series ◽  
Temporal Features ◽  
Proposed Model ◽  
Time Series Segmentation ◽  
Similarity Model ◽  
Dynamic Time ◽  
Investment Portfolios

Finding the correlation between stocks is an effective method for screening and adjusting investment portfolios for investors. One single temporal feature or static nontemporal features are generally used in most studies to measure the similarity between stocks. However, these features are not sufficient to explore phenomena such as price fluctuations similar in shape but unequal in length which may be caused by multiple temporal features. To research stock price volatilities entirely, mining the correlation between stocks should be considered from the point view of multiple features described as time series, including closing price, etc. In this paper, a time-sensitive composite similarity model designed for multivariate time-series correlation analysis based on dynamic time warping is proposed. First, a stock is chosen as the benchmark, and the multivariate time series are segmented by the peaks and troughs time-series segmentation (PTS) algorithm. Second, similar stocks are screened out by similarity. Finally, the rate of rising or falling together between stock pairs is used to verify the proposed model’s effectiveness. Compared with other models, the composite similarity model brings in multiple temporal features and is generalizable for numerical multivariate time series in different fields. The results show that the proposed model is very promising.

scholarly journals Spatial Time Series Segmentation Method Based on Traveling Waves

2014 ◽  
Vol 14 (10) ◽  
Author(s):  
Alexander Trofimov ◽  
Igor Kolodkin ◽  
Vadim Ushakov ◽  
Boris Velichkovsky
Keyword(s):  
Time Series ◽  
Traveling Waves ◽  
Segmentation Method ◽  
Time Series Segmentation

Hinging Hyperplanes for Time-Series Segmentation

2013 ◽  
Vol 24 (8) ◽  
pp. 1279-1291 ◽  
Author(s):  
Xiaolin Huang ◽  
Marin Matijas ◽  
Johan A. K. Suykens
Keyword(s):  
Time Series ◽  
Time Series Segmentation

Comparing the Macroeconomic Responses of US and Japan through Time Series Segmentation

2013 ◽  
pp. 79-93
Author(s):  
Jian Cheng Wong ◽  
Gladys Hui Ting Lee ◽  
Yiting Zhang ◽  
Woei Shyr Yim ◽  
Robert Paulo Fornia ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Segmentation

Representation learning for unsupervised heterogeneous multivariate time series segmentation and its application

2019 ◽  
Vol 130 ◽  
pp. 272-281 ◽  
Author(s):  
Hyunjoong Kim ◽  
Han Kyul Kim ◽  
Misuk Kim ◽  
Jooseoung Park ◽  
Sungzoon Cho ◽  
...  
Keyword(s):  
Time Series ◽  
Multivariate Time Series ◽  
Representation Learning ◽  
Time Series Segmentation
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