Gold Prices Time-Series Forecasting: Comparison of Statistical Techniques

Indra Maryati, Christian Christian, Adi Suryaputra Paramita
Pages 372-381
Abstract Views 852
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DOI https://doi.org/10.47738/jads.v4i4.135

Abstract


The fluctuation of gold prices throughout the year makes it difficult for both investors and regular individuals to predict the future value. The goal of this research is to utilize various statistical techniques, such as linear regression, naive bayes, and various types of smoothing algorithms, to predict the price of gold. The data used in this study was obtained from Kaggle and is from a 70-year time period. The results showed that using a single exponential smoothing method had the highest accuracy and precision, with a good MAPE score of 7.12%. This study is unique in that it compares multiple algorithms using data over a long time period, and it can be useful for investors and traders in making decisions related to gold prices. Additionally, it can also serve as a reference for future research studies.

Keywords


time series; gold prices; linear regression; exponential smoothing;

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DOI: https://doi.org/10.47738/jads.v4i4.135

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Journal of Applied Data Sciences

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