Flow Field Forecasting with Many Predictors
Kyle A. Caudle, Patrick S. Fleming, Larry D. Pyeatt, Randy C. Hoover · 2019
Flow field (FF) forecasting is a statistical framework for generalpurpose 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 paper we show 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 forecasting technique can outperform many of the traditional techniques, especially when the time series data is non-stationary.