Univariate Time Series Forecasting with Fuzzy CMAC

Daming Shi; Junbin Gao; R. Tilanil

Univariate Time Series Forecasting with Fuzzy CMAC

Daming Shi, Junbin Gao, R. Tilanil

Research output: Book chapter/Published conference paper › Conference paper › peer-review

4 Citations (Scopus)

32 Downloads (Pure)

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
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
Country/Territory	China
Period	26/08/04 → 29/08/04

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Cite this

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title = "Univariate Time Series Forecasting with Fuzzy CMAC",

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.",

keywords = "Open access version available, CMAC, Fuzzy, Neural networks, Univariate time series forecasting",

author = "Daming Shi and Junbin Gao and R. Tilanil",

note = "Imported on 03 May 2017 - DigiTool details were: publisher = USA: IEEE, 2004. Event dates (773o) = 26-29 August 2004; Parent title (773t) = International Conference on Machine Learning and Cybernetics.; International Conference on Machine Learning and Cybernetics ; Conference date: 26-08-2004 Through 29-08-2004",

year = "2004",

language = "English",

volume = "7",

pages = "4166--4170",

booktitle = "IEEE Proceedings of 3rd, 2004 International Conference on Machine Learning and Cybernetics.",

publisher = "IEEE",

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TY - GEN

T1 - Univariate Time Series Forecasting with Fuzzy CMAC

AU - Shi, Daming

AU - Gao, Junbin

AU - Tilanil, R.

N1 - Imported on 03 May 2017 - DigiTool details were: publisher = USA: IEEE, 2004. Event dates (773o) = 26-29 August 2004; Parent title (773t) = International Conference on Machine Learning and Cybernetics.

PY - 2004

Y1 - 2004

N2 - 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.

AB - 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.

KW - Open access version available

KW - CMAC

KW - Fuzzy

KW - Neural networks

KW - Univariate time series forecasting

M3 - Conference paper

VL - 7

SP - 4166

EP - 4170

BT - IEEE Proceedings of 3rd, 2004 International Conference on Machine Learning and Cybernetics.

PB - IEEE

CY - USA

T2 - International Conference on Machine Learning and Cybernetics

Y2 - 26 August 2004 through 29 August 2004

ER -

Univariate Time Series Forecasting with Fuzzy CMAC

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