Time-Series Forecasting Using Autoregression Enhanced k-Nearest Neighbors Method

doi:10.1007/s12204-013-1418-x

上海交通大学学报（英文版） ›› 2013, Vol. 18 ›› Issue (4): 434-442.doi: 10.1007/s12204-013-1418-x

Time-Series Forecasting Using Autoregression Enhanced k-Nearest Neighbors Method

PAN Feng1* (潘峰), ZHAO Hai-bo2 (赵海波), LIU Hua-shan1 (刘华山)

(1. School of Information Science and Technology, Donghua University, Shanghai 200051, China; 2. Institute of Aircraft Equipment, Naval Academy of Armament, Shanghai 200436, China)

出版日期:2013-08-28 发布日期:2013-08-12
通讯作者: PAN Feng(潘峰) E-mail:fpan@dhu.edu.cn

Time-Series Forecasting Using Autoregression Enhanced k-Nearest Neighbors Method

PAN Feng1* (潘峰), ZHAO Hai-bo2 (赵海波), LIU Hua-shan1 (刘华山)

(1. School of Information Science and Technology, Donghua University, Shanghai 200051, China; 2. Institute of Aircraft Equipment, Naval Academy of Armament, Shanghai 200436, China)

Online:2013-08-28 Published:2013-08-12
Contact: PAN Feng(潘峰) E-mail:fpan@dhu.edu.cn

摘要/Abstract

摘要： This study proposes two metrics using the nearest neighbors method to improve the accuracy of time-series forecasting. These two metrics can be treated as a hybrid forecasting approach to combine linear and non-linear forecasting techniques. One metric redefines the distance in k-nearest neighbors based on the coefficients of autoregression (AR) in time series. Meanwhile, an improvement to Kulesh’s adaptive metrics in the nearest neighbors is also presented. To evaluate the performance of the two proposed metrics, three types of time-series data, namely deterministic synthetic data, chaotic time-series data and real time-series data, are predicted. Experimental results show the superiority of the proposed AR-enhanced k-nearest neighbors methods to the traditional k-nearest neighbors metric and Kulesh’s adaptive metrics.

关键词: time series, forecasting, nearest neighbors method, autoregression (AR), metrics

Abstract: This study proposes two metrics using the nearest neighbors method to improve the accuracy of time-series forecasting. These two metrics can be treated as a hybrid forecasting approach to combine linear and non-linear forecasting techniques. One metric redefines the distance in k-nearest neighbors based on the coefficients of autoregression (AR) in time series. Meanwhile, an improvement to Kulesh’s adaptive metrics in the nearest neighbors is also presented. To evaluate the performance of the two proposed metrics, three types of time-series data, namely deterministic synthetic data, chaotic time-series data and real time-series data, are predicted. Experimental results show the superiority of the proposed AR-enhanced k-nearest neighbors methods to the traditional k-nearest neighbors metric and Kulesh’s adaptive metrics.

Key words: time series, forecasting, nearest neighbors method, autoregression (AR), metrics

中图分类号:

TP 391.4

PAN Feng1* (潘峰), ZHAO Hai-bo2 (赵海波), LIU Hua-shan1 (刘华山). Time-Series Forecasting Using Autoregression Enhanced k-Nearest Neighbors Method[J]. 上海交通大学学报（英文版）, 2013, 18(4): 434-442.

PAN Feng1* (潘峰), ZHAO Hai-bo2 (赵海波), LIU Hua-shan1 (刘华山). Time-Series Forecasting Using Autoregression Enhanced k-Nearest Neighbors Method[J]. Journal of shanghai Jiaotong University (Science), 2013, 18(4): 434-442.

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Time-Series Forecasting Using Autoregression Enhanced k-Nearest Neighbors Method

Time-Series Forecasting Using Autoregression Enhanced k-Nearest Neighbors Method

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