Effectiveness of Single and Double Exponential Smoothing: SES, ARRSES and Holt’s Linear for Time Series Data Prediction with Trend and Non-seasonal Characteristic (Covid-19 Vaccinate Case)

Efektivitas Metode Exponential Smoothing untuk Prediksi Data Runtun Waktu Pola Tren dan Non-musiman (Studi Kasus Cakupan Vaksinasi Covid-19)

Authors

  • Wiwik Wiyanti Statistics Department, Matana University

DOI:

https://doi.org/10.20956/j.v20i1.27193

Keywords:

exponential, SES, ARRSES, forecast, holt’s

Abstract

Purpose of this research is effectiveness the exponential smoothing for predict the time series data which has trend and non-seasonal characteristic. In this research using data case vaccinate of covid-19 1st, 2nd, 3rd and 4th. The advantage of this research is we can choose the best method for this type data. The methodology of this research is quantitative, with analyze data method is exponential smoothing (SES, ARRSES, and HOLT’S linear). The result of data analyze is the predict error for four vaccinate data are very close to zero. SES gets 0% to 0,73%, ARRSES gets 0% to 0,66% and Holt’s linear gets 0% to 0,29%. The result of this research can conclude that exponential smoothing method effective for predict the data with trend and non-seasonal data.

 

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Published

2023-09-06

How to Cite

Wiyanti, W. (2023). Effectiveness of Single and Double Exponential Smoothing: SES, ARRSES and Holt’s Linear for Time Series Data Prediction with Trend and Non-seasonal Characteristic (Covid-19 Vaccinate Case): Efektivitas Metode Exponential Smoothing untuk Prediksi Data Runtun Waktu Pola Tren dan Non-musiman (Studi Kasus Cakupan Vaksinasi Covid-19). Jurnal Matematika, Statistika Dan Komputasi, 20(1), 52–64. https://doi.org/10.20956/j.v20i1.27193

Issue

Vol. 20 No. 1 (2023): SEPTEMBER, 2023

Section

Research Articles

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