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

Ivette Luna and Rosangela Ballini

International Journal of Forecasting, 2011, vol. 27, issue 3, 708-724

Abstract: 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.

Keywords: Simulation; Rule-based; forecasting; Forecasting; competitions; Disaggregation; Fuzzy; inference; system; Adaptive; fuzzy; systems (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:27:y::i:3:p:708-724

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