Distance variable improvement of time-series big data stream evaluation

Journal of Big Data

Ari Wibisono¹  Valian Fil Ahli¹  Petrus Mursanto¹  May Iffah Rizki¹  Lintang Matahari Hasani¹  Jihan Adibah¹  Wendy D. W. T. Bayu¹ 

[1] Faculty of Computer Science, Universitas Indonesia, Indonesia, Kampus, UI Depok;

关键词: Intelligent Systems;    Data stream;    Distance improvement;    Big data regression;   

DOI  :  10.1186/s40537-020-00359-w

来源: DOAJ

【 摘 要 】

Abstract Real-time information mining of a big dataset consisting of time series data is a very challenging task. For this purpose, we propose using the mean distance and the standard deviation to enhance the accuracy of the existing fast incremental model tree with the drift detection (FIMT-DD) algorithm. The standard FIMT-DD algorithm uses the Hoeffding bound as its splitting criterion. We propose the further use of the mean distance and standard deviation, which are used to split a tree more accurately than the standard method. We verify our proposed method using the large Traffic Demand Dataset, which consists of 4,000,000 instances; Tennet’s big wind power plant dataset, which consists of 435,268 instances; and a road weather dataset, which consists of 30,000,000 instances. The results show that our proposed FIMT-DD algorithm improves the accuracy compared to the standard method and Chernoff bound approach. The measured errors demonstrate that our approach results in a lower Mean Absolute Percentage Error (MAPE) in every stage of learning by approximately 2.49% compared with the Chernoff Bound method and 19.65% compared with the standard method.

【 授权许可】

Unknown