期刊
INTERNATIONAL JOURNAL OF FORECASTING
卷 27, 期 3, 页码 708-724出版社
ELSEVIER
DOI: 10.1016/j.ijforecast.2010.09.006
关键词
Simulation; Rule-based forecasting; Forecasting competitions; Disaggregation; Fuzzy inference system; Adaptive fuzzy systems
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. (C) 2010 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.
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