Stability analysis of time series forecasting with ART models
Alexei Bocharov, David M. Chickering, David E. Heckerman · WIT transactions on information and communication technologies · 2006
Time Series (TS) analysis based on autoregressive tree models has been introduced in Meek et al. (2002).In addition to features present in the original design, the current SQL 2005 implementation also addressed the "forecasting instability" phenomenon that has been observed on a range of highly nonstationary and/or non-linear data sets.In technical terms the cases of long range forecasting instability are characterized by rapid growth of the mean absolute prediction error with time, which may or may not be accompanied by significant growth of the predicted standard deviation.In practice, the cases of instability where predicted standard deviation stays tame are especially misleading, since they can furnish unreliable predictions with little or no visual cues that would characterize them as unreliable.The method described in this paper is designed to detect and control the long range forecasting instabilities and to cull the unreliable predictions.