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Effective business planning and decision-making depend on accurate sales forecasting. In this study, two well-known forecasting techniques—Power BI and ARIMA (AutoRegressive Integrated Moving Average)—are compared. Traditional time series models like ARIMA concentrate on examining historical data to predict future sales trends, but Power BI is a flexible business intelligence platform that blends advanced forecasting methods with data visualisation. The study evaluates both systems based on criteria such forecasting precision, usefulness, adaptability, and applicability in real-world business situations. This study examines the advantages and disadvantages of each strategy by examining actual sales data, offering insights into how well-suited each is for various forecasting needs.

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Sales Data Forecasting Using Arima Model

  • 会议论文

  • 发表日期：

    2025年

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  • V. Nithya,
  • Shivani Rajesh,
  • S. Nithyasri

摘要

Effective business planning and decision-making depend on accurate sales forecasting. In this study, two well-known forecasting techniques—Power BI and ARIMA (AutoRegressive Integrated Moving Average)—are compared. Traditional time series models like ARIMA concentrate on examining historical data to predict future sales trends, but Power BI is a flexible business intelligence platform that blends advanced forecasting methods with data visualisation. The study evaluates both systems based on criteria such forecasting precision, usefulness, adaptability, and applicability in real-world business situations. This study examines the advantages and disadvantages of each strategy by examining actual sales data, offering insights into how well-suited each is for various forecasting needs.