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

Saime Şule Aksakal; Erol Eğrioğlu

Makale detayları

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

Yazarlar:

Saime Şule Aksakal | Erol Eğrioğlu

Yayıncı:

Giresun University

Ana Başlıklar:

Computational Statistics

Dergi:

Turkish Journal of Forecasting Vol.9 Issue 2, pp.44-50

Belge Tipi:

Makale

Yayın Tarihi:

9/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001359310

pISSN:

2618-6594

Anahtar kelimeler:

Özet:

 Exponential smoothing methods have been commonly used for time series forecasting. Holts 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.

 Exponential smoothing methods have been commonly used for time series forecasting. Holts 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.

 Exponential smoothing methods have been commonly used for time series forecasting. Holts 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.

 Exponential smoothing methods have been commonly used for time series forecasting. Holts 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.

Makale detayları

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

Yazarlar:

Saime Şule Aksakal | Erol Eğrioğlu

Yayıncı:

Giresun University

Ana Başlıklar:

Computational Statistics

Dergi:

Turkish Journal of Forecasting Vol.9 Issue 2, pp.44-50

Belge Tipi:

Makale

Yayın Tarihi:

9/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001359310

pISSN:

2618-6594

Forecasting

Exponential Smoothing

Holt’s Linear Trend Method

Particle Swarm Optimization

Sayı

Makale detayları

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

Yazarlar: Saime Şule Aksakal | Erol Eğrioğlu

Yayıncı: Giresun University

Ana Başlıklar: Computational Statistics

Dergi: Turkish Journal of Forecasting Vol.9 Issue 2, pp.44-50

Belge Tipi: Makale

Yayın Tarihi: 9/2025

Belge Dili: İngilizce

Document ID (Org No.): 2001359310

pISSN: 2618-6594

Forecasting

Exponential Smoothing

Holt’s Linear Trend Method

Particle Swarm Optimization

Sayı

Yazarlar:

Saime Şule Aksakal | Erol Eğrioğlu

Yayıncı:

Giresun University

Ana Başlıklar:

Computational Statistics

Dergi:

Turkish Journal of Forecasting Vol.9 Issue 2, pp.44-50

Belge Tipi:

Makale

Yayın Tarihi:

9/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001359310

pISSN:

2618-6594