Combining wavelet‐based feature extractions with relevance vector machines for stock index forecasting

The relevance vector machine (RVM) is a Bayesian version of the support vector machine, which with a sparse model representation has appeared to be a powerful tool for time‐series forecasting. The RVM has demonstrated better performance over other methods such as neural networks or autoregressive integrated moving average based models. This study proposes a hybrid model that combines wavelet‐based feature extractions with RVM models to forecast stock indices. The time series of explanatory variables are decomposed using some wavelet bases and the extracted time‐scale features serve as inputs of an RVM to perform the non‐parametric regression and forecasting. Compared with traditional forecasting models, our proposed method performs best. The root‐mean‐squared forecasting errors are significantly reduced.