Forecasting Fund Purchase Amount Using Arima Model

Rui  He

Forecasting Fund Purchase Amount Using Arima Model

Rui He （ Department of Sydney Smart Technology College, Northeastern University at Qinhuangdao, Qinhuangdao, Hebei Province, 066004, China ）

https://doi.org/10.37155/2972-4813-0202-6

Abstract

Predicting fund purchase amounts is a significant challenge for economists and statisticians. Accurate predictions can lead to favourable outcomes for both companies and individuals. This paper proposes an ARIMA model to extract insights from the data and provide a method for forecasting fund purchase amounts over the coming days. We begin by examining the characteristics and structure of the dataset, calculating various statistical indices. Subsequently, we implement data transformation strategies to address outliers and missing values. Using R software, we analyse the data and develop several candidate ARIMA models. The most suitable model is selected based on the Autocorrelation Function (ACF), Partial Autocorrelation Function (PACF), and the Akaike Information Criterion (AIC). Finally, we evaluate the chosen model using Mean Squared Error (MSE) and various residual analysis plots. The residuals from our model indicate strong performance in predicting fund purchase amounts.

Keywords

ARIMA, data analysis , fund purchase amount, prediction.

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References

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Publishing time:2024-06-30
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