The forecasting for prices of consumer goods using time series methods Mongolia
DOI:
https://doi.org/10.5564/jimdt.v6i1.3762Keywords:
Time series analysis, Correlation analysis, ARIMAAbstract
Time series analysis is important in forecasting price fluctuations of consumer goods, which directly influence economic stability. This study examines the price trends of essential commodities in Ulaanbaatar, Mongolia, from 2012 to 2022. The dataset, sourced from the National Statistical Office of Mongolia, includes prices for 11 key items: flour, bread, rice, beef, milk, yogurt, sugar, eggs, apples, potatoes, and A92 gasoline. A combination of correlation analysis and time series forecasting was used to understand the relationships between goods and to predict future price trends. The results revealed high correlations between certain products, such as eggs and milk, and highlighted the influence of seasonal factors and random movements on prices. Time series models provided accurate predictions for some goods like flour and milk, while others, such as apples and potatoes, showed larger forecast errors. These insights are valuable for policymakers and businesses to anticipate price changes and make informed decisions in economic planning.
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