A Hybrid Forecasting Method Based on The Exponential Smoothing and Multiplicative Neuron Model Artificial Neural Network
Abstract
Holt exponential smoothing method is an effective method for forecasting of non-seasonal time series.
In Holt method, moving average operator with exponential decay weights is used. Multiplicative
neuron model artificial neural network is a popular artificial neural network type and it has been also
successfully used for the aim of forecasting of non-seasonal time series. In this study, a hybrid
forecasting method that combines the properties of both Holt exponential smoothing method and
multiplicative neuron model artificial neural network is proposed. The parameters and combination
weights for Holt method and multiplicative neuron model are determined by particle swarm
optimization. The final forecasts and confidence intervals for forecasts are obtained by using random
subsampling bootstrap method. Moreover, hypothesis tests for combination weights are applied by
using bootstrap samples. The proposed method is applied to Dow-Jones Industrial average stock
exchange data sets between the years 2010 and 2012 and the forecasting performance of proposed
method is compared with other some other methods in the literature.
Keywords
References
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Details
Primary Language
English
Subjects
Statistical Data Science
Journal Section
Research Article
Publication Date
September 30, 2025
Submission Date
September 27, 2025
Acceptance Date
September 28, 2025
Published in Issue
Year 2025 Volume: 9 Number: 2
APA
Aksakal, S. Ş., & Eğrioğlu, E. (2025). A Hybrid Forecasting Method Based on The Exponential Smoothing and Multiplicative Neuron Model Artificial Neural Network. Turkish Journal of Forecasting, 9(2), 37-43. https://doi.org/10.34110/forecasting.1792231
AMA
1.Aksakal SŞ, Eğrioğlu E. A Hybrid Forecasting Method Based on The Exponential Smoothing and Multiplicative Neuron Model Artificial Neural Network. TJF. 2025;9(2):37-43. doi:10.34110/forecasting.1792231
Chicago
Aksakal, Saime Şule, and Erol Eğrioğlu. 2025. “A Hybrid Forecasting Method Based on The Exponential Smoothing and Multiplicative Neuron Model Artificial Neural Network”. Turkish Journal of Forecasting 9 (2): 37-43. https://doi.org/10.34110/forecasting.1792231.
EndNote
Aksakal SŞ, Eğrioğlu E (September 1, 2025) A Hybrid Forecasting Method Based on The Exponential Smoothing and Multiplicative Neuron Model Artificial Neural Network. Turkish Journal of Forecasting 9 2 37–43.
IEEE
[1]S. Ş. Aksakal and E. Eğrioğlu, “A Hybrid Forecasting Method Based on The Exponential Smoothing and Multiplicative Neuron Model Artificial Neural Network”, TJF, vol. 9, no. 2, pp. 37–43, Sept. 2025, doi: 10.34110/forecasting.1792231.
ISNAD
Aksakal, Saime Şule - Eğrioğlu, Erol. “A Hybrid Forecasting Method Based on The Exponential Smoothing and Multiplicative Neuron Model Artificial Neural Network”. Turkish Journal of Forecasting 9/2 (September 1, 2025): 37-43. https://doi.org/10.34110/forecasting.1792231.
JAMA
1.Aksakal SŞ, Eğrioğlu E. A Hybrid Forecasting Method Based on The Exponential Smoothing and Multiplicative Neuron Model Artificial Neural Network. TJF. 2025;9:37–43.
MLA
Aksakal, Saime Şule, and Erol Eğrioğlu. “A Hybrid Forecasting Method Based on The Exponential Smoothing and Multiplicative Neuron Model Artificial Neural Network”. Turkish Journal of Forecasting, vol. 9, no. 2, Sept. 2025, pp. 37-43, doi:10.34110/forecasting.1792231.
Vancouver
1.Saime Şule Aksakal, Erol Eğrioğlu. A Hybrid Forecasting Method Based on The Exponential Smoothing and Multiplicative Neuron Model Artificial Neural Network. TJF. 2025 Sep. 1;9(2):37-43. doi:10.34110/forecasting.1792231