Brown's weighted exponential moving average implementation in forex forecasting

Abstract

In 2016, a time series forecasting technique which combined the weighting factor calculation formula found in weighted moving average with Brown's double exponential smoothing procedures had been introduced. The technique is known as Brown's weighted exponential moving average (B-WEMA), as a new variant of double exponential smoothing method which does the exponential filter processes twice. In this research, we will try to implement the new method to forecast some foreign exchange, or known as forex data, including EUR/USD, AUD/USD, GBP/USD, USD/JPY, and EUR/JPY data. The time series data forecasting results using B-WEMA then be compared with other conventional and hybrid moving average methods, such as weighted moving average (WMA), exponential moving average (EMA), and Brown's double exponential smoothing (B-DES). The comparison results show that B-WEMA has a better accuracy level than other forecasting methods used in this research.