Abstract
Double Exponential Smoothing is an improvement of Simple Exponential Smoothing, also known as Exponential Moving Average, which does the exponential filter process twice. It’s usually been used to predict the future data in time series analysis, where there is a trend in the data. In this paper, we aim to introduce a new approach of Brown’s Double Exponential Smoothing in time series analysis. The new approach will combine the calculation of weighting factor in Weighted Moving Average and implement the results with Brown’s Double Exponential Smoothing method. The proposed method will be tested on Jakarta Stock Exchange (JKSE) composite index data. The result of the proposed method shows a promising result in this work.
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