Financial market forecasting by integrating wavelet transform and K-means clustering with support vector machine

Financial market forecasting is a challenging problem and researchers are still exploring the ways to improve the performance of the existing models. This paper presents a forecasting model by integrating wavelet transform, K-means clustering with support vector machine. At the first stage, noise of the input prices is removed by using wavelet denoising. Wavelet multi resolution analysis is used to decompose the original time series in to multiple details and approximated decompositions. Individual support vector models are trained for each detail part. Approximated part is further analyzed by clustering and training support vector models for each cluster. Finally the forecast is made for the wavelet denoised time series by summing up the forecasts of each support vector model. Results have shown that the proposed model has given the accurate forecast and has the capability to support decisions in real world trading