caa a22 4500 605477795 CHVBK 20210128100402.0 cr unu---uuuuu 210128e20151001xx s 000 0 eng 10.1007/s10994-014-5460-1 doi (NATIONALLICENCE)springer-10.1007/s10994-014-5460-1 Using causal discovery for feature selection in multivariate numerical time series [Elektronische Daten] [Youqiang Sun, Jiuyong Li, Jixue Liu, Christopher Chow, Bingyu Sun, Rujing Wang] Time series data contains temporal ordering, which makes its feature selection different from the normal feature selection. Feature selection in multivariate time series has two tasks: identifying the relevant features and finding their effective window sizes of lagged values. The methods extended from normal feature selection methods do not solve this two-dimensional feature selection problem since they do not take lagged observations of features into consideration. In this paper, we present a method using the Granger causality discovery to identify causal features with effective sliding window sizes in multivariate numerical time series. The proposed method considers the influence of lagged observations of features on the target time series. We compare our proposed feature selection method with several normal feature selection methods on multivariate time series data using three well-known modeling methods. Our method outperforms other methods for predicting future values of target time series. In a real world case study on water quality monitoring data, we show that the features selected by our method contain four out of five features used by domain experts, and prediction performance on our features is better than that on features of domain experts using three modeling methods. The Author(s), 2014 Feature selection nationallicence Multivariate time series nationallicence Causal discovery nationallicence Prediction and regression nationallicence Granger causality nationallicence Sun Youqiang School of Information Science and Technology, University of Science and Technology of China, Hefei, Anhui, China aut Li Jiuyong School of Information Technology and Mathematical Sciences, University of South Australia, Adelaide, SA, Australia aut Liu Jixue School of Information Technology and Mathematical Sciences, University of South Australia, Adelaide, SA, Australia aut Chow Christopher Australian Water Quality Centre, SA Water, Adelaide, SA, Australia aut Sun Bingyu School of Information Science and Technology, University of Science and Technology of China, Hefei, Anhui, China aut Wang Rujing School of Information Science and Technology, University of Science and Technology of China, Hefei, Anhui, China aut Machine Learning Springer US; http://www.springer-ny.com 101/1-3(2015-10-01), 377-395 0885-6125 101:1-3<377 2015 101 10994 https://doi.org/10.1007/s10994-014-5460-1 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s10994-014-5460-1 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Sun Youqiang School of Information Science and Technology, University of Science and Technology of China, Hefei, Anhui, China aut NATIONALLICENCE 700 1- Li Jiuyong School of Information Technology and Mathematical Sciences, University of South Australia, Adelaide, SA, Australia aut NATIONALLICENCE 700 1- Liu Jixue School of Information Technology and Mathematical Sciences, University of South Australia, Adelaide, SA, Australia aut NATIONALLICENCE 700 1- Chow Christopher Australian Water Quality Centre, SA Water, Adelaide, SA, Australia aut NATIONALLICENCE 700 1- Sun Bingyu School of Information Science and Technology, University of Science and Technology of China, Hefei, Anhui, China aut NATIONALLICENCE 700 1- Wang Rujing School of Information Science and Technology, University of Science and Technology of China, Hefei, Anhui, China aut NATIONALLICENCE 773 0- Machine Learning Springer US; http://www.springer-ny.com 101/1-3(2015-10-01), 377-395 0885-6125 101:1-3<377 2015 101 10994