Time Series Prediction as a Problem of Missing Values
Antti Sorjamaa, Amaury Lendasse · 2007
Abstract. In this paper, time series prediction is considered as a problem of missing values. A new method for the determination of the missing time series values is presented. The new method is based on two projection methods: a nonlinear one (Self-Organized Maps) and a linear one (Empirical Orthogonal Functions). The presented global methodology combines the advantages of both methods to get accurate candidates for prediction values. The methods are applied to a time series competition dataset. 1