Bayesian analysis of time series using granular computing approach

• We review applications of the soft computing techniques in the statistical time series analysis.
• We propose the Bayesian granular computing approach for time series forecasting.
• The employed data mining and classification methods provide useful information for forecasting.
• We build the prior model probability distributions taking advantage of the information granules.
• The proposed approach provides accurate forecasts and additional, human-consistent information.

The soft computing methods, especially data mining, usually enable to describe large datasets in a human-consistent way with the use of some generic and conceptually meaningful information entities like information granules. However, such information granules may be applied not only for the descriptive purposes, but also for prediction. We review the main developments and challenges of the application of the soft computing methods in the time series analysis and forecasting, and we provide a conceptual framework for the Bayesian time series forecasting using the granular computing approach. Within the proposed approach, the information granules are successfully incorporated into the Bayesian posterior simulation process. The approach is evaluated with a set of experiments on the artificial and benchmark real-life time series datasets.