Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Journals

remove_circle_outline

Article Types

Countries / Regions

remove_circle_outline

Search Results (1)

Search Parameters:
Keywords = exponentially weighed moving average

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
18 pages, 6253 KB  
Article
Comparative Study on Exponentially Weighted Moving Average Approaches for the Self-Starting Forecasting
by Jaehong Yu, Seoung Bum Kim, Jinli Bai and Sung Won Han
Appl. Sci. 2020, 10(20), 7351; https://doi.org/10.3390/app10207351 - 20 Oct 2020
Cited by 25 | Viewed by 8866
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Big Data and AI for Process Innovation in the Industry 4.0 Era)
Show Figures

Figure 1

Back to TopTop