A Comparative Study Between Holt's Double Exponential Smoothing and Fuzzy Time Series Markov Chain in Gold Price Forecasting

Abstract

Gold price is important to a country’s economy as it can be used as a hedge against inflation especially during financial turmoil. Besides, the gold price also has an impact on the stock market price. As an investor, to make a good investment plan, information regarding the fluctuation price of gold is necessary to minimize the risk. Therefore, this study proposes to compare two of the forecasting models, namely Holt's Double Exponential Smoothing and Fuzzy Time Series Markov Chain to forecast the price of gold. Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) are used to determine a better forecasting model with smaller error. Initially, the data price of gold is analysed by using Durbin Watson Test to check the suitability of the data for time series analysis. The finding of this study shows that Fuzzy Time Series Markov Chain is more accurate in predicting gold price as compared to Holt’s Double Exponential Smoothing because it produces smaller values of RMSE and MAPE.

Keywords

• Holt's Double Explonential Smoothing
• RMSE
• MAPE
• Fuzzy Time Series
• Markov Chain
• Gold Price