Time Series Forecasting Model for Supermarket Sales using FB-Prophet

• Published in: 2021 5th International Conference on Computing Methodologies and Communication (ICCMC) pp. 547 - 554
• Main Authors: Kumar Jha, Bineet, Pande, Shilpa
• Format: Conference Proceeding
• Language: English
• Published: IEEE 08-04-2021
• Subjects: additive model; Additives; ARIMA; Computational modeling; Facebook prophet; Predictive models; Scalability; Time series analysis; time series forecasting; Training; Transfer learning
• DOI: 10.1109/ICCMC51019.2021.9418033

Forecasting techniques are used in the various problem domains such as- sales, banking, healthcare, stock market, etc. The time-series dataset has time-related information that is useful for prediction and statistical analysis. The supermarket sales prediction helps improve sales in a business environment. The technique helps in decision making in a problem domain. Many tools are available for forecasting such as the regression model, Logistic exponential model. The Facebook (FB) Prophet is the latest tool that has shown an improved performance in terms of accuracy of prediction. This research work has proposed a FB Prophet tool for the sales prediction of the supermarket data. The proposed research work has examined few forecasting models such as- The additive model, the Autoregressive integrated moving average (ARIMA) model, FB Prophet model. From the propsoed research work, it is concluded that, FB Prophet is a better prediction model in terms of low error, better prediction, and better fitting.