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Type: Artigo de periódico
Title: Top-down strategies based on adaptive fuzzy rule-based systems for daily time series forecasting
Author: Luna, I
Ballini, R
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. (C) 2010 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.
Subject: Simulation
Rule-based forecasting
Forecasting competitions
Fuzzy inference system
Adaptive fuzzy systems
Country: Holanda
Editor: Elsevier Science Bv
Rights: fechado
Identifier DOI: 10.1016/j.ijforecast.2010.09.006
Date Issue: 2011
Appears in Collections:Unicamp - Artigos e Outros Documentos

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