Closest History Flow Field Forecasting for IEEE CSCI-ISCS

Regression is an often used, general means of time series forecasting. Flow field (FF) forecasting formalizes this approach, explicitly emphasizing both the chosen form of regression and the choice of predictors. FF forecasting has been previously implemented on univariate time series using Gaussian process regression. This paper addresses the practically important bivariate case, proposing a form of FF forecasting in which the flow field interpolation is accomplished by an application of nearest-neighbors regression. We call this implementation Closest history-flow field (CH-FF). CH-FF forecasting involves distance comparisons among spaces with differing dimensions. A bias adjustment is proposed to compensate for these differences, and CH-FF forecasting's resulting performance is compared in computer experiments with commonly practiced bivariate forecasting methodologies. Estimators of forecast standard error are also proposed.