Time series forecasting;
SARIMAX;
Heavy-tailed distribution;
Regression tree;
Random forest;
62M10;
62M20;
68T07;
D O I:
暂无
中图分类号:
学科分类号:
摘要:
Time series forecasting has been the area of intensive research for years. Statistical, machine learning or mixed approaches have been proposed to handle this one of the most challenging tasks. However, little research has been devoted to tackle the frequently appearing assumption of normality of given data. In our research, we aim to extend the time series forecasting models for heavy-tailed distribution of noise. In this paper, we focused on normal and Student’s t distributed time series. The SARIMAX model (with maximum likelihood approach) is compared with the regression tree-based method—random forest. The research covers not only forecasts but also prediction intervals, which often have hugely informative value as far as practical applications are concerned. Although our study is focused on the selected models, the presented problem is universal and the proposed approach can be discussed in the context of other systems.
机构:
Northwestern Polytech Univ, Unmanned Syst Res Inst, Xian 710072, Peoples R China
Xian Modern Control Technol Res Inst, Xian 710065, Peoples R ChinaNorthwestern Polytech Univ, Unmanned Syst Res Inst, Xian 710072, Peoples R China
Chang, Guan-Nan
Fu, Wen-Xing
论文数: 0引用数: 0
h-index: 0
机构:
Northwestern Polytech Univ, Unmanned Syst Res Inst, Xian 710072, Peoples R ChinaNorthwestern Polytech Univ, Unmanned Syst Res Inst, Xian 710072, Peoples R China
Fu, Wen-Xing
Cui, Tao
论文数: 0引用数: 0
h-index: 0
机构:
Shanghai Jiao Tong Univ, Sch Aeronaut & Astronaut, Shanghai 200240, Peoples R ChinaNorthwestern Polytech Univ, Unmanned Syst Res Inst, Xian 710072, Peoples R China
Cui, Tao
Song, Ling-Yun
论文数: 0引用数: 0
h-index: 0
机构:
Shanghai Jiao Tong Univ, Sch Aeronaut & Astronaut, Shanghai 200240, Peoples R ChinaNorthwestern Polytech Univ, Unmanned Syst Res Inst, Xian 710072, Peoples R China