Comparative Study on Exponentially Weighted Moving Average Approaches for the Self-Starting Forecasting
Recently, a number of data analysists have suffered from an insufficiency of historical observations in many real situations. To address the insufficiency of historical observations, self-starting forecasting process can be used. A self-starting forecasting process continuously updates the base mode...
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doaj-6d02994c1084468a8c5a279e5dfb65e42020-11-25T03:56:36ZengMDPI AGApplied Sciences2076-34172020-10-01107351735110.3390/app10207351Comparative Study on Exponentially Weighted Moving Average Approaches for the Self-Starting ForecastingJaehong Yu0Seoung Bum Kim1Jinli Bai2Sung Won Han3Department of Industrial and Management Engineering, Incheon National University, Incheon 22012, KoreaSchool of Industrial Management Engineering, Korea University—Seoul, Seoul 04620, KoreaSchool of Industrial Management Engineering, Korea University—Seoul, Seoul 04620, KoreaSchool of Industrial Management Engineering, Korea University—Seoul, Seoul 04620, KoreaRecently, a number of data analysists have suffered from an insufficiency of historical observations in many real situations. To address the insufficiency of historical observations, self-starting forecasting process can be used. A self-starting forecasting process continuously updates the base models as new observations are newly recorded, and it helps to cope with inaccurate prediction caused by the insufficiency of historical observations. This study compared the properties of several exponentially weighted moving average methods as base models for the self-starting forecasting process. Exponentially weighted moving average methods are the most widely used forecasting techniques because of their superior performance as well as computational efficiency. In this study, we compared the performance of a self-starting forecasting process using different existing exponentially weighted moving average methods under various simulation scenarios and real case datasets. Through this study, we can provide the guideline for determining which exponentially weighted moving average method works best for the self-starting forecasting process.https://www.mdpi.com/2076-3417/10/20/7351comparative studyexponentially weighed moving averagenon-stationary time seriesself-starting forecasting |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jaehong Yu Seoung Bum Kim Jinli Bai Sung Won Han |
spellingShingle |
Jaehong Yu Seoung Bum Kim Jinli Bai Sung Won Han Comparative Study on Exponentially Weighted Moving Average Approaches for the Self-Starting Forecasting Applied Sciences comparative study exponentially weighed moving average non-stationary time series self-starting forecasting |
author_facet |
Jaehong Yu Seoung Bum Kim Jinli Bai Sung Won Han |
author_sort |
Jaehong Yu |
title |
Comparative Study on Exponentially Weighted Moving Average Approaches for the Self-Starting Forecasting |
title_short |
Comparative Study on Exponentially Weighted Moving Average Approaches for the Self-Starting Forecasting |
title_full |
Comparative Study on Exponentially Weighted Moving Average Approaches for the Self-Starting Forecasting |
title_fullStr |
Comparative Study on Exponentially Weighted Moving Average Approaches for the Self-Starting Forecasting |
title_full_unstemmed |
Comparative Study on Exponentially Weighted Moving Average Approaches for the Self-Starting Forecasting |
title_sort |
comparative study on exponentially weighted moving average approaches for the self-starting forecasting |
publisher |
MDPI AG |
series |
Applied Sciences |
issn |
2076-3417 |
publishDate |
2020-10-01 |
description |
Recently, a number of data analysists have suffered from an insufficiency of historical observations in many real situations. To address the insufficiency of historical observations, self-starting forecasting process can be used. A self-starting forecasting process continuously updates the base models as new observations are newly recorded, and it helps to cope with inaccurate prediction caused by the insufficiency of historical observations. This study compared the properties of several exponentially weighted moving average methods as base models for the self-starting forecasting process. Exponentially weighted moving average methods are the most widely used forecasting techniques because of their superior performance as well as computational efficiency. In this study, we compared the performance of a self-starting forecasting process using different existing exponentially weighted moving average methods under various simulation scenarios and real case datasets. Through this study, we can provide the guideline for determining which exponentially weighted moving average method works best for the self-starting forecasting process. |
topic |
comparative study exponentially weighed moving average non-stationary time series self-starting forecasting |
url |
https://www.mdpi.com/2076-3417/10/20/7351 |
work_keys_str_mv |
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