Online nonstationary time series prediction using sparse coding with dictionary update

Online nonstationary time series prediction requires continuous model parameter update to be able to track the changing characteristics of the data. Recent approaches such as sequential parameter adaptation and varying coefficient models require re-estimation of the prediction model parameter with every new available data. An online dictionary-based prediction approach is proposed in this paper where new data is added to the dictionary while maintaining the dictionary size by using 3 different methods. The dictionary based prediction employs a sparse coding model where an L1-norm convex optimization problem is solved for each new time series vector, while no training phase is required. The validity of the proposed approach is tested on 4 nonstationary time series data sets reported in the literature. Results show that the proposed online prediction approach reaches comparable results with more complex techniques which require more computationally demanding parameter adaptation.