Stability Analysis Of Time Series Forecasting With
ART Models
Author(s)
A. Bocharov, D. Chickering & D. Heckerman
Abstract
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 non-stationary
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.
Keywords: time series, forecasting, autoregression, stability, data mining.
1 Autoregressive tree (ART) models
Consider historic time series data for multiple time series. For each target
(predictable) series, we build autoregressive tree model, which is effectively a
collection of autoregressive models [2] and where the boundaries between the
applicable models are defined by binary decision trees (see subsection 2.1 of the
research paper [1] for detailed definition and examples of ART models).
When underlying decision tree for a target time series Y consists of more than
one leaf node, then each of its interior nodes is defined by an input variable V
Keywords
time series, forecasting, autoregression, stability, data mining.
Related Book
Other papers in this volume
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