Nonstationary time series prediction combined with slow feature analysis

作者：Wang, G.^*; Chen, X.

来源：Nonlinear Processes in Geophysics, 2015, 22(4): 377-382.

DOI：10.5194/npg-22-377-2015

• TOTAL OZONE
• CLIMATE
• DYNAMICS
• FORCES

Almost all climate time series have some degree of nonstationarity due to external driving forces perturbing the observed system. Therefore, these external driving forces should be taken into account when constructing the climate dynamics. This paper presents a new technique of obtaining the driving forces of a time series from the slow feature analysis (SFA) approach, and then introduces them into a predictive model to predict nonstationary time series. The basic theory of the technique is to consider the driving forces as state variables and to incorporate them into the predictive model. Experiments using a modified logistic time series and winter ozone data in Arosa, Switzerland, were conducted to test the model. The results showed improved prediction skills.

• 出版日期2015
• 单位中国科学院大气物理研究所