Top-down strategies based on adaptive fuzzy rule-based systems for daily time series forecasting
AbstractThis 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.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Bibliographic InfoArticle provided by Elsevier in its journal International Journal of Forecasting.
Volume (Year): 27 (2011)
Issue (Month): 3 ()
Contact details of provider:
Web page: http://www.elsevier.com/locate/ijforecast
Simulation; Rule-based forecasting; Forecasting competitions; Disaggregation; Fuzzy inference system; Adaptive fuzzy systems;
Other versions of this item:
- 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, July.
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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.
- 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.
- 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.
- 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.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Zhang, Lei).
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.