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Thursday, May 17
Applications
Time Series Modeling
Thu, May 17, 5:15 PM - 6:15 PM
Lake Fairfax A
 

Forecasting with Many Predictors (304553)

Presentation

*Kyle Caudle, SD School of Mines and Technology 
Patrick Fleming, SD School of Mines and Technology 
Larry Pyeatt, SD School of Mines and Technology 

Keywords: time series, forecasting, regression trees

Flow field (FF) forecasting is a statistical framework for general-purpose time series forecasting that can be readily adapted to various applications. Given a historical time series space, FF forecasting can actively search the space and determine which variables are most useful in prediction. FF forecasting was first developed as a univariate forecasting technique, but was extended to bivariate time series. In this talk it will be shown that FF forecasting can further be extended to higher dimensional time series involving potentially hundreds of predictor variables. We call this implementation Tree based-flow field (TB-FF) forecasting. Using a tree-based algorithm, we sift through the predictor space in order to find the best predictor variables in a large candidate pool. We show that TB-FF can outperform many of the traditional techniques especially when the time series data is non-stationary.