Time series forecasting using clustering with periodic pattern

Time series forecasting have attracted a great deal of attention from various research communities. One of the method which improves accuracy of forecasting is time series clustering. The contribution of this work is a new method of clustering which relies on finding periodic pattern by splitting the time series into two subsequences (clusters) with lower potential error of prediction then whole series. Having such subsequences we predict their values separately with methods customized to the specificities of the subsequences and then merge results according to the pattern and obtain prediction of original time series. In order to check efficiency of our approach we perform analysis of various artificial data sets. We also present a real data set for which application of our approach gives more then 300% improvement in accuracy of prediction. We show that in artificially created series we obtain even more pronounced accuracy improvement. Additionally our approach can be use to noise filtering. In our work we consider noise of a periodic repetitive pattern and we present simulation where we find correct series from data where 50% of elements is random noise.