A Naïve Support Vector Regression Benchmark for the NN3 Forecasting Competition

Support Vector Regression is one of the promising contenders in predicting the 111 time series of the NN3 Neural Forecasting Competition. As they offer substantial degrees of freedom in the modeling process, in selecting the kernel function and its parameters, cost and epsilon parameters, issues of model parameterization and model selection arise. In lack of an established methodology or comprehensive empirical evidence on their modeling, a number of heuristics and ad-hoc rules have emerged, that result in selecting different models, which show different performance. In order to determine a lower bound for Support Vector Regression accuracy in the NN3 competition, this paper seeks to compute benchmark results using a naive methodology with a fixed parameter grid-search and exponentially increasing step sizes for radial basis function kernels, estimating 43,725 candidate models for each of the 111 time series. The naive approach attempts to mimic many of the common mistakes in model building, providing error as a lower bound to support vector regression accuracy. The in-sample results parameters are evaluated to estimate the impact of potential shortcomings in the grid search heuristic and the interaction effects of the parameters.