An evolving algorithm based on unobservable components neuro-fuzzy model for time series forecasting

The forecasting and characterization of time series are very useful for experts to take appropriate decisions, to plan actions and to understand the time series patterns. However, there are a small number of methods that consider both objectives at the same time. In this paper, an algorithm for nonstationary and seasonal time series forecasting, with an evolving neuro-fuzzy Takagi-Sugeno (NF-TS) structure, is proposed. For this algorithm, the NF-TS inputs are unobservable patterns extracted from the time series by a decomposition technique. As experiment, a real seasonal time series was used to compare the forecasting performance of these proposed algorithm with an other similar NF-TS, whose inputs were formed by autoregressive data from the same time series. When there is available observations from time series, the NF-TS evolves its structure and adapt its parameters. If the data is not available, the proposed methodology needs to forecast the next value. In order to extract the unobservable components from time series, the Holt-Winters method optimized by Particle Swarm Optimization (PSO) approach was considered in this experiment.