Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/4781
Title: Unvariate Time Series Forecasting with Fuzzy CMAC
Contributor(s): Shi, Da-Ming (author); Gao, Junbin (author); Tilani, Raveen (author)
Publication Date: 2004
Open Access: Yes
Handle Link: https://hdl.handle.net/1959.11/4781
Abstract: In financial and business areas, forecasting is a necessary tool that enables decision makers to predict changes in demands, plans and sales. This work 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.
Publication Type: Conference Publication
Conference Name: 2004 International Conference on Machine Learning and Cybernetics, Shanghai, China, 26 - 29th August, 2004
Conference Details: 2004 International Conference on Machine Learning and Cybernetics, Shanghai, China, 26 - 29th August, 2004
Source of Publication: Proceedings of 2004 International Conference on Machine Learning and Cybernetics, v.7, p. 4166-4170
Publisher: IEEE: Institute of Electrical and Electronics Engineers
Place of Publication: United States of America
Field of Research (FOR): 080108 Neural, Evolutionary and Fuzzy Computation
Socio-Economic Outcome Codes: 890205 Information Processing Services (incl. Data Entry and Capture)
HERDC Category Description: E2 Non-Refereed Scholarly Conference Publication
Other Links: http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=1384570
http://trove.nla.gov.au/work/19896212
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