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Journals / Turkish Journal of Forecasting / 2025 / Volume: 9 Issue: 2

Modified Holt’s Linear Trend Method Based on Particle Swarm Optimization

Saime Şule Aksakal(Giresun Üniversitesi)0000-0002-1810-1040
Erol Eğrioğlu(GIRESUN UNIVERSITY)0000-0003-4301-4149
Pages
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Publication Date
2025-09-30
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Strong and weak sides of the modified Holt method are investigated by using real-world data sets and simulated data sets.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Exponential smoothing methods have been commonly used for time series forecasting.
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Key Findings & Data:Holt’s linear trend exponential smoothing is a well-known exponential smoothing method and it can give successful forecasting results for time series which have trend component.
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Conclusion & Impact:Strong and weak sides of the modified Holt method are investigated by using real-world data sets and simulated data sets.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Forecasting · Exponential Smoothing · Holt’s Linear Trend Method

Abstract

Exponential smoothing methods have been commonly used for time series forecasting. Holt’s linear trend exponential smoothing is a well-known exponential smoothing method and it can give successful forecasting results for time series which have trend component. In this study, a new modified Holt method is introduced. In modified Holt method, update formulas have second order lagged terms apart from classical Holt method. Moreover, initial values for trend and level and smoothing parameters are estimated by using particle swarm optimization. Strong and weak sides of the modified Holt method are investigated by using real-world data sets and simulated data sets.

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Cite This Article

Aksakal, S. Ş., Eğrioğlu, E. (2025). Modified Holt’s Linear Trend Method Based on Particle Swarm Optimization. Turkish Journal of Forecasting, Volume: 9 Issue: 2. https://doi.org/10.34110/forecasting.1792276

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