Top‐down strategies based on adaptive fuzzy rule‐based systems for daily time series forecasting

This paper presents a data‐driven approach applied to the long term prediction of daily time series in the Neural Forecasting Competition. The proposal comprises the use of adaptive fuzzy rule‐based systems in a top‐down modeling framework. Therefore, daily samples are aggregated to build weekly time series, and consequently, model optimization is performed in a top‐down framework, thus reducing the forecast horizon from 56 to 8 steps ahead. Two different disaggregation procedures are evaluated: the historical and daily top‐down approaches. Data pre‐processing and input selection are carried out prior to the model adjustment. The prediction results are validated using multiple time series, as well as rolling origin evaluations with model re‐calibration, and the results are compared with those obtained using daily models, allowing us to analyze the effectiveness of the top‐down approach for longer forecast horizons.