Data mining of time series using stacked generalizers

Abstract

Data mining is the search for valuable information in large volumes of data. Finding patterns in time series databases is important to a variety of applications, including stock market trading and budget forecasting. This paper reports on an extension of neural network methods for planning and budgeting in the State of Utah. In particular, historical time series are analyzed using stacked generalization, a methodology devised to aid in developing models that generalize well to future time periods. Stacked generalization is compared to ARIMA and to stand-alone neural networks. The results are consistent and suggest promise for the stacked generalization method in other time series domains.