A Sequential Hybrid Forecasting System for Demand Prediction-Reference-Cited by-同舟云学术

A Sequential Hybrid Forecasting System for Demand Prediction

Published: Issue: Volume: Page:518-532
ISSN:0302-9743
Container-title:Machine Learning and Data Mining in Pattern Recognition
language:
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Author:

Aburto Luis,Weber Richard

Publisher

Springer Berlin Heidelberg

Link

http://link.springer.com/content/pdf/10.1007/978-3-540-73499-4_39.pdf

Reference21 articles.

1. Abraham, A., Nath, B.: A neuro-fuzzy approach for modeling electricity demand in Victoria. Applied Soft Computing 1(2), 127–138 (2001)

2. Abraham, A., Philip, S., Mahanti, P.K.: Soft Computing Models for Weather Forecasting. International Journal of Applied Science and Computations 11(3), 106–117 (2004)

3. Aburto, L., Weber, R.: Improved Supply Chain Management based on Hybrid Demand Forecasts. Applied Soft Computing 7, 136–144 (2007)

4. Akaike, H., Kitagawa, G. (eds.): The Practice of Time Series Analysis. Springer, Berlin (1999)

5. Akaike, H.: A new look at the statistical model identification. IEEE Trans. Automatic Control AC-19, 716–723 (1974)

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