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Machine Learning Model to Predict Gold Prices for Zimbabwean Economy
Abstract
This Research tackled this insight into Gold prices by producing a predictive Machine Learning Model based on ARIMA, Seasonal Naïve and SARIMA algorithms. These algorithms were extensively evaluated in their performance to predict- Gold prices in Zimbabwe as a key aspect of this Research. Factors impacting on the fluctuation of Gold prices especially in the Zimbabwean economy were also to be identified in the course of the Research to provide suitable variables akin to model performance. Results garnered from the course of the research clearly point out that ARIMA and SARIMA models proved more effective in the analysis of stationery time series data and these models produced a confidence level of above 96% on model fitness indicating high levels of model performance. Limitations were identified in past data availability for effective model training and recommendations were made for future data recordings and collation.
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