{"title":"时间序列数据挖掘中感知重要点识别的改进算法","authors":"Tak-Chung Fu, Y. Hung, F. Chung","doi":"10.1109/ISCMI.2017.8279589","DOIUrl":null,"url":null,"abstract":"In the field of time series data mining, the concept of the Perceptually Important Point (PIP) identification process is proposed for financial time series pattern matching and it is then found suitable for time series dimensionality reduction and representation. Its strength is on preserving the overall shape of the time series by identifying the salient points in it. With the rise of Big Data, time series data contributes a major proportion, especially on the data which generates by sensors in the Internet of Things (IoT) environment. According to the nature of PIP identification and the successful applications in the past years, it is worth to further explore the opportunity to apply PIP in time series “Big Data”. However, the performance of PIP identification is always considered as the limitation when dealing with “Big” time series data. In this paper, two improvement algorithms namely Caching and Splitting algorithms are proposed. Significant improvement in term of speed is obtained by these improvement algorithms.","PeriodicalId":119111,"journal":{"name":"2017 IEEE 4th International Conference on Soft Computing & Machine Intelligence (ISCMI)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":"{\"title\":\"Improvement algorithms of perceptually important point identification for time series data mining\",\"authors\":\"Tak-Chung Fu, Y. Hung, F. Chung\",\"doi\":\"10.1109/ISCMI.2017.8279589\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In the field of time series data mining, the concept of the Perceptually Important Point (PIP) identification process is proposed for financial time series pattern matching and it is then found suitable for time series dimensionality reduction and representation. Its strength is on preserving the overall shape of the time series by identifying the salient points in it. With the rise of Big Data, time series data contributes a major proportion, especially on the data which generates by sensors in the Internet of Things (IoT) environment. According to the nature of PIP identification and the successful applications in the past years, it is worth to further explore the opportunity to apply PIP in time series “Big Data”. However, the performance of PIP identification is always considered as the limitation when dealing with “Big” time series data. In this paper, two improvement algorithms namely Caching and Splitting algorithms are proposed. Significant improvement in term of speed is obtained by these improvement algorithms.\",\"PeriodicalId\":119111,\"journal\":{\"name\":\"2017 IEEE 4th International Conference on Soft Computing & Machine Intelligence (ISCMI)\",\"volume\":\"94 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"13\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 IEEE 4th International Conference on Soft Computing & Machine Intelligence (ISCMI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISCMI.2017.8279589\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE 4th International Conference on Soft Computing & Machine Intelligence (ISCMI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISCMI.2017.8279589","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 13
摘要
在时间序列数据挖掘领域,提出了用于金融时间序列模式匹配的感知重要点(percepperceptual Important Point, PIP)识别过程的概念,并发现它适用于时间序列降维和表示。它的优势在于通过识别时间序列中的突出点来保持时间序列的整体形状。随着大数据的兴起,时间序列数据占了很大的比重,特别是在物联网(IoT)环境中传感器产生的数据中。根据PIP识别的性质和过去几年的成功应用,值得进一步探索PIP在时间序列“大数据”中的应用机会。然而,在处理“大”时间序列数据时,PIP识别的性能一直被认为是一个限制。本文提出了两种改进算法:缓存算法和分割算法。这些改进算法在速度方面取得了显著的提高。
Improvement algorithms of perceptually important point identification for time series data mining
In the field of time series data mining, the concept of the Perceptually Important Point (PIP) identification process is proposed for financial time series pattern matching and it is then found suitable for time series dimensionality reduction and representation. Its strength is on preserving the overall shape of the time series by identifying the salient points in it. With the rise of Big Data, time series data contributes a major proportion, especially on the data which generates by sensors in the Internet of Things (IoT) environment. According to the nature of PIP identification and the successful applications in the past years, it is worth to further explore the opportunity to apply PIP in time series “Big Data”. However, the performance of PIP identification is always considered as the limitation when dealing with “Big” time series data. In this paper, two improvement algorithms namely Caching and Splitting algorithms are proposed. Significant improvement in term of speed is obtained by these improvement algorithms.