Time Series Forecasting Using Holt-Winters Exponential Smoothing: Application to Abaca Fiber Data

Pleños, Mary

doi:10.22630/prs.2022.22.2.6

Cited by 7 publications

(5 citation statements)

References 8 publications

(10 reference statements)

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“…Holt-Winters Exponential Smoothing merupakan salah satu teknik yang digunakan untuk melakukan peramalan terhadap data time series yang menunjukkan adanya tren dan musim. Metode ini merupakan pengembangan dari teknik Holt dan Winters [1], [2].…”

Section: Metodeunclassified

Penentuan Nilai Terkecil Root Mean Squarred Error (Rmse) Metode Holt-Winters Exponential Smoothing Pada Ekspor Kopi Tujuan Jerman

Achmadin,

Retnowardani,

Mashitasari

et al. 2024

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Penelitian ini mengkaji dinamika ekspor kopi dari Indonesia ke Jerman dengan menerapkan metode Holt-Winters Exponential Smoothing untuk proses peramalan. Fokus penelitian hanya akan dibatasi pada penentuan nilai Root Mean Squared Error (RMSE) terkecil. Tujuan utama penelitian adalah memberikan pemahaman yang mendalam mengenai tren, pola musiman, dan kinerja keseluruhan ekspor kopi dari tahun 2000 hingga 2022. Dengan memanfaatkan data ekspor historis yang diperoleh dari Badan Pusat Statistik Indonesia, penelitian menguji efektivitas berbagai kombinasi parameter (α, β, dan ɣ) dalam metode Holt-Winters untuk mendapatkan prediksi yang akurat. Penelitian mempertimbangkan berbagai faktor dengan menyesuaikan nilai-nilai parameter ini dalam kisaran 0,1 hingga 0,9 untuk meminimalkan nilai Root Mean Squared Error (RMSE). Melalui visualisasi grafis dan analisis statistik, penelitian mengidentifikasi kombinasi parameter optimal yang menghasilkan RMSE terendah, yang menunjukkan prediksi yang paling akurat dalam peramalan.

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Section: Metodeunclassified

Penentuan Nilai Terkecil Root Mean Squarred Error (Rmse) Metode Holt-Winters Exponential Smoothing Pada Ekspor Kopi Tujuan Jerman

Achmadin,

Retnowardani,

Mashitasari

et al. 2024

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“…Goodwin (2010) also provided an easy-to-follow example illustrating this method of exponential smoothing. For detailed discussion of both the Holt's and Holt-Winters methods, see Kalekar (2004).…”

Section:  Time Seriesmentioning

confidence: 99%

Quantitative Technology Forecasting: A Review of Trend Extrapolation Methods

Tsai

Berleant

Segall

et al. 2023

Int. J. Innovation Technol. Management

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Quantitative technology forecasting uses quantitative methods to understand and project technological changes. It is a broad field encompassing many different techniques and has been applied to a vast range of technologies. A widely used approach in this field is trend extrapolation. Based on the literature available to us, there has been little or no attempt made to systematically review the empirical evidence on quantitative trend extrapolation techniques. This study attempts to close this gap by conducting a systematic review of the technology forecasting literature addressing the application of quantitative trend extrapolation techniques. We identified 25 studies relevant to the objective of this research and classified the techniques used in the studies into different categories, among which the growth curves and time series methods were shown to remain popular over the past decade while the newer methods, such as machine learning-based hybrid models, have emerged in recent years. As more effort and evidence are needed to determine if hybrid models are superior to traditional methods, we expect a growing trend in the development and application of hybrid models to technology forecasting.

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“…The concept is to assign exponentially diminishing weights to more recent instances. As we proceed back in time, we will witness decreasing weights [14,15] .…”

Section: Holt Winter's Exponential Smoothing (Hwes)mentioning

confidence: 99%

Analysis and Forecasting of Solar Power Generation using Machine Learning

Andrianajaiana,

DavidRazafimahefa,

Cristian-Gyozo

et al. 2023

IFR

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Current photovoltaic systems are equipped with a monitoring system. The data is recorded with a predefined time base. The operator has a large amount of data. This article is a contribution to the analysis and understanding of this data. It proposes to estimate Generation from this data using machine learning. The study was conducted at the 1Mw generation site in Miroslava, Iasi, Romania. The visualization and interpretation of the generation data is presented in this article. Then, prediction techniques are presented to obtain an estimate of the plant’s generation. These techniques are: Simple Exponential Smoothing (SES), Autoregression (AR), Seasonal Auto-Regressive Integrated Moving Average with eXogenous factors (SARIMA), Holt Winter’s Exponential Smoothing (HWES), Long Short Term Memory neural (LSTM) and Convolutional Neural Network (CNN).

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Time Series Forecasting Using Holt-Winters Exponential Smoothing: Application to Abaca Fiber Data

Cited by 7 publications

References 8 publications

Penentuan Nilai Terkecil Root Mean Squarred Error (Rmse) Metode Holt-Winters Exponential Smoothing Pada Ekspor Kopi Tujuan Jerman

Penentuan Nilai Terkecil Root Mean Squarred Error (Rmse) Metode Holt-Winters Exponential Smoothing Pada Ekspor Kopi Tujuan Jerman

Quantitative Technology Forecasting: A Review of Trend Extrapolation Methods

Analysis and Forecasting of Solar Power Generation using Machine Learning

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