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Golden exponential smoothing: a self-adjusted method for identifying optimum alpha / Foo Fong Yeng, Azrina Suhaimi and Soo Kum Yoke

The conventional double exponential smoothing is a forecasting method that troubles the forecaster with a tremendous choice of its parameter, alpha. The choice of alpha would greatly influence the accuracy of prediction. In this paper, an integrated forecasting method named Golden Exponential Smooth...

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Main Authors: Yeng, Foo Fong, Suhaimi, Azrina, Yoke, Soo Kum
Format: Article
Language:English
Published: Universiti Teknologi MARA 2020
Subjects:
Mathematical statistics. Probabilities
Online Access:http://ir.uitm.edu.my/id/eprint/48125/1/48125.pdf
http://ir.uitm.edu.my/id/eprint/48125/
https://mjoc.uitm.edu.my
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spelling my.uitm.ir.481252021-06-24T09:47:07Z http://ir.uitm.edu.my/id/eprint/48125/ Golden exponential smoothing: a self-adjusted method for identifying optimum alpha / Foo Fong Yeng, Azrina Suhaimi and Soo Kum Yoke Yeng, Foo Fong Suhaimi, Azrina Yoke, Soo Kum Mathematical statistics. Probabilities The conventional double exponential smoothing is a forecasting method that troubles the forecaster with a tremendous choice of its parameter, alpha. The choice of alpha would greatly influence the accuracy of prediction. In this paper, an integrated forecasting method named Golden Exponential Smoothing (GES) is proposed to solve the problem of choosing the optimum alpha. The conventional method needs human intervention in which the forecaster would determine the most suitable alpha or else the prediction accuracy will be affected. This method is reformed and interposed with Golden Section Search such that an optimum alpha could be identified during the algorithm training process. Numerical simulations of four sets of times series data are employed to test the efficiency of the GES model. The findings show that the GES model is self-adjusted according to the situation and converged fast in the algorithm training process. The optimum alpha, which is identified from the algorithm training stage, demonstrates good performance in the stage of Model Testing and Usage. Universiti Teknologi MARA 2020-10 Article PeerReviewed text en http://ir.uitm.edu.my/id/eprint/48125/1/48125.pdf ID48125 Yeng, Foo Fong and Suhaimi, Azrina and Yoke, Soo Kum (2020) Golden exponential smoothing: a self-adjusted method for identifying optimum alpha / Foo Fong Yeng, Azrina Suhaimi and Soo Kum Yoke. Malaysian Journal of Computing (MJoC), 5 (2). pp. 587-596. ISSN (eISSN): 2600-8238 https://mjoc.uitm.edu.my
institution Universiti Teknologi Mara
building Tun Abdul Razak Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Mara
content_source UiTM Institutional Repository
url_provider http://ir.uitm.edu.my/
language English
topic Mathematical statistics. Probabilities
spellingShingle Mathematical statistics. Probabilities
Yeng, Foo Fong
Suhaimi, Azrina
Yoke, Soo Kum
Golden exponential smoothing: a self-adjusted method for identifying optimum alpha / Foo Fong Yeng, Azrina Suhaimi and Soo Kum Yoke
description The conventional double exponential smoothing is a forecasting method that troubles the forecaster with a tremendous choice of its parameter, alpha. The choice of alpha would greatly influence the accuracy of prediction. In this paper, an integrated forecasting method named Golden Exponential Smoothing (GES) is proposed to solve the problem of choosing the optimum alpha. The conventional method needs human intervention in which the forecaster would determine the most suitable alpha or else the prediction accuracy will be affected. This method is reformed and interposed with Golden Section Search such that an optimum alpha could be identified during the algorithm training process. Numerical simulations of four sets of times series data are employed to test the efficiency of the GES model. The findings show that the GES model is self-adjusted according to the situation and converged fast in the algorithm training process. The optimum alpha, which is identified from the algorithm training stage, demonstrates good performance in the stage of Model Testing and Usage.
format Article
author Yeng, Foo Fong
Suhaimi, Azrina
Yoke, Soo Kum
author_facet Yeng, Foo Fong
Suhaimi, Azrina
Yoke, Soo Kum
author_sort Yeng, Foo Fong
title Golden exponential smoothing: a self-adjusted method for identifying optimum alpha / Foo Fong Yeng, Azrina Suhaimi and Soo Kum Yoke
title_short Golden exponential smoothing: a self-adjusted method for identifying optimum alpha / Foo Fong Yeng, Azrina Suhaimi and Soo Kum Yoke
title_full Golden exponential smoothing: a self-adjusted method for identifying optimum alpha / Foo Fong Yeng, Azrina Suhaimi and Soo Kum Yoke
title_fullStr Golden exponential smoothing: a self-adjusted method for identifying optimum alpha / Foo Fong Yeng, Azrina Suhaimi and Soo Kum Yoke
title_full_unstemmed Golden exponential smoothing: a self-adjusted method for identifying optimum alpha / Foo Fong Yeng, Azrina Suhaimi and Soo Kum Yoke
title_sort golden exponential smoothing: a self-adjusted method for identifying optimum alpha / foo fong yeng, azrina suhaimi and soo kum yoke
publisher Universiti Teknologi MARA
publishDate 2020
url http://ir.uitm.edu.my/id/eprint/48125/1/48125.pdf
http://ir.uitm.edu.my/id/eprint/48125/
https://mjoc.uitm.edu.my
_version_ 1703963508449738752
score 13.211869

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