Multivariate Time Series Local Support Vector Regression Forecast Methods for Daily Temperature

Authors

Dingcheng Wang, College of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, China;
Zhili Cao, College of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, China;
Beijing Chen, College of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, China;
Yujia Ni, College of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, China;

Abstract

Abstract: Daily temperature forecast is an important part in weather forecasting. New methods are explored to improve the prediction accuracy since the atmosphere system is a complex nonlinear system. A model based on multivariate time series and local support vector regression was proposed. After constructing multivariate time-series with C-C method and minimum prediction error method, a way to extract the nearest neighbor from each predictor's sequences was used to build the 1-day ahead local forecasting model for daily maximum and minimum temperature. To demonstrate the effectiveness, the model was applied and tested in data from 753 stations data package of China. Simulation results show that the model can improve prediction accuracy effectively, and has a better application value in short-term daily temperature forecast compared with univariate time series.

Recommended Citation

Wang, Dingcheng; Cao, Zhili; Chen, Beijing; and Ni, Yujia (2020) "Multivariate Time Series Local Support Vector Regression Forecast Methods for Daily Temperature," Journal of System Simulation: Vol. 28: Iss. 3, Article 20.
Available at: https://dc-china-simulation.researchcommons.org/journal/vol28/iss3/20

First Page

654

Revised Date

2015-01-11

DOI Link

https://doi.org/

Last Page

660

CLC

TP391.9

Recommended Citation

Wang Dingcheng, Cao Zhili, Chen Beijing, Ni Yujia. Multivariate Time Series Local Support Vector Regression Forecast Methods for Daily Temperature[J]. Journal of System Simulation, 2016, 28(3): 654-660.