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

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  • Luna, Ivette
  • Ballini, Rosangela

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.

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  • Luna, Ivette & Ballini, Rosangela, 2011. "Top-down strategies based on adaptive fuzzy rule-based systems for daily time series forecasting," International Journal of Forecasting, Elsevier, vol. 27(3), pages 708-724.
    • Handle: RePEc:eee:intfor:v:27:y:2011:i:3:p:708-724
    DOI: 10.1016/j.ijforecast.2010.09.006
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    1. Tashman, Leonard J., 2000. "Out-of-sample tests of forecasting accuracy: an analysis and review," International Journal of Forecasting, Elsevier, vol. 16(4), pages 437-450.
    2. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
    3. Widiarta, Handik & Viswanathan, S. & Piplani, Rajesh, 2009. "Forecasting aggregate demand: An analytical evaluation of top-down versus bottom-up forecasting in a production planning framework," International Journal of Production Economics, Elsevier, vol. 118(1), pages 87-94, March.
    4. Ghiassi, M. & Saidane, H. & Zimbra, D.K., 2005. "A dynamic artificial neural network model for forecasting time series events," International Journal of Forecasting, Elsevier, vol. 21(2), pages 341-362.
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    Cited by:

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    3. Athanasopoulos, George & Hyndman, Rob J. & Kourentzes, Nikolaos & Panagiotelis, Anastasios, 2024. "Forecast reconciliation: A review," International Journal of Forecasting, Elsevier, vol. 40(2), pages 430-456.
    4. Pournader, Mehrdokht & Ghaderi, Hadi & Hassanzadegan, Amir & Fahimnia, Behnam, 2021. "Artificial intelligence applications in supply chain management," International Journal of Production Economics, Elsevier, vol. 241(C).
    5. Bahman Rostami‐Tabar & M. Zied Babai & Aris Syntetos & Yves Ducq, 2013. "Demand forecasting by temporal aggregation," Naval Research Logistics (NRL), John Wiley & Sons, vol. 60(6), pages 479-498, September.
    6. Yslene Kachba & Daiane Maria de Genaro Chiroli & Jônatas T. Belotti & Thiago Antonini Alves & Yara de Souza Tadano & Hugo Siqueira, 2020. "Artificial Neural Networks to Estimate the Influence of Vehicular Emission Variables on Morbidity and Mortality in the Largest Metropolis in South America," Sustainability, MDPI, vol. 12(7), pages 1-15, March.
    7. Hugo Siqueira & Mariana Macedo & Yara de Souza Tadano & Thiago Antonini Alves & Sergio L. Stevan & Domingos S. Oliveira & Manoel H.N. Marinho & Paulo S.G. de Mattos Neto & João F. L. de Oliveira & Ive, 2020. "Selection of Temporal Lags for Predicting Riverflow Series from Hydroelectric Plants Using Variable Selection Methods," Energies, MDPI, vol. 13(16), pages 1-35, August.

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