Time-series forecasting through wavelets transformation and a mixture of expert models
This paper describes a system formed by a mixture of expert models (MEM) for time-series forecasting. We deal with several different competing models, such as partial least squares, K-nearest neighbours and carbon copy. The input space, after changing its base using the Haar wavelets transform, is partitioned into disjoint regions by a clustering algorithm. For each region, a benchmark is performed among the different competing models aiming at selecting the most adequate one. MEM has improved the forecast performance when compared with the single models as experimentally demonstrated through two different time series: laser data and exchange rate data.
time-series forecasting wavelets mixture of experts partial least squares