Abstract
In financial and business areas, forecasting is a necessary tool that enables decision makers to predict changes in demands, plans and sales. This paper applies a novel Fuzzy Cerebellar-Model-Articulation-Controller (FCMAC) into univariate time-series forecasting and investigates its performance in comparison to established techniques such as Single Exponential Smoothing, Holt's Linear Trend, Holt-Winter's Additive and Multiplicative methods and the Box-Jenkin's ARIMA model. Experimental results from the M3 Competition data reveal that the FCMAC model yielded lower errors for certain data sets. The conditions under which the FCMAC model emerged superior are discussed.
Original language | English |
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Title of host publication | IEEE Proceedings of 3rd, 2004 International Conference on Machine Learning and Cybernetics. |
Place of Publication | USA |
Publisher | IEEE |
Pages | 4166-4170 |
Number of pages | 5 |
Volume | 7 |
ISBN (Electronic) | 0780384032 |
Publication status | Published - 2004 |
Event | International Conference on Machine Learning and Cybernetics - Shanghai, China, China Duration: 26 Aug 2004 → 29 Aug 2004 |
Conference
Conference | International Conference on Machine Learning and Cybernetics |
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Country | China |
Period | 26/08/04 → 29/08/04 |