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This study uses TS (time series) analysis techniques to create accurate forecasting models and insights using historical sales data from a superstore. The dataset's comprehensive coverage allows for the identification of trends, seasonality, and other temporal patterns. The research employs two well-known approaches, ARIMA and Prophet, to apply these techniques to Superstore sales data. TS data is widely used in various industries, including retail, and is essential for efficient inventory control, resource distribution, and tactical decision-making. The research utilizes data mining methods to identify basics and seasonal trends in past sales data, then uses ML (machine learning) algorithms, including Prophet and ARIMA (Auto-Regressive Integrated Moving Average) models, to forecast projected sales. The outcome enhances our understanding of TS analysis and forecasting techniques and helps optimize Superstore operations. ARIMA and Prophet demonstrate their ability to capture complex sales data dynamics, providing valuable insights for companies in a fast-paced, dynamic setting.

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A Novel Approach of Superstore Sales Data by EDA and ARIMA

  • 会议论文

  • 发表日期：

    2026年

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  • J. Bhoomika Reddy,
  • N. Ashritha,
  • Katiki Reddy Navya,
  • V. Kakulapati,
  • V. Monica

摘要

This study uses TS (time series) analysis techniques to create accurate forecasting models and insights using historical sales data from a superstore. The dataset's comprehensive coverage allows for the identification of trends, seasonality, and other temporal patterns. The research employs two well-known approaches, ARIMA and Prophet, to apply these techniques to Superstore sales data. TS data is widely used in various industries, including retail, and is essential for efficient inventory control, resource distribution, and tactical decision-making. The research utilizes data mining methods to identify basics and seasonal trends in past sales data, then uses ML (machine learning) algorithms, including Prophet and ARIMA (Auto-Regressive Integrated Moving Average) models, to forecast projected sales. The outcome enhances our understanding of TS analysis and forecasting techniques and helps optimize Superstore operations. ARIMA and Prophet demonstrate their ability to capture complex sales data dynamics, providing valuable insights for companies in a fast-paced, dynamic setting.