Univariate Time Series Forecasting with Fuzzy CMAC

Daming Shi, Junbin Gao, R. Tilanil

Research output: Book chapter/Published conference paperConference paperpeer-review

4 Citations (Scopus)
27 Downloads (Pure)


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 languageEnglish
Title of host publicationIEEE Proceedings of 3rd, 2004 International Conference on Machine Learning and Cybernetics.
Place of PublicationUSA
Number of pages5
ISBN (Electronic)0780384032
Publication statusPublished - 2004
EventInternational Conference on Machine Learning and Cybernetics - Shanghai, China, China
Duration: 26 Aug 200429 Aug 2004


ConferenceInternational Conference on Machine Learning and Cybernetics

Fingerprint Dive into the research topics of 'Univariate Time Series Forecasting with Fuzzy CMAC'. Together they form a unique fingerprint.

Cite this