Nonstationary time series prediction combined with slow feature analysis
- 1Key Laboratory of Middle Atmosphere and Global Environment Observations, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
- 2Pingtan Meteorological Bureau of Fujian Province, Pingtan 350400, China
Abstract. 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.