A Comparative Study on Forecasting of Retail Sales
Comparative study of ARIMA, Facebook Prophet, and LightGBM models for forecasting Walmart retail sales using M5 dataset.
What it examines
This paper benchmarks forecasting models on Walmart's historical sales data to predict future sales. It compares ARIMA, Facebook Prophet, and LightGBM models, highlighting their performance and computational efficiency in handling large datasets.
What it concludes
The research concludes that ARIMA provides the best accuracy, while LightGBM offers a balance of accuracy and computational efficiency. Future work could involve developing ensemble models to further optimize performance. Potential applications include inventory management and sales forecasting for large retail companies.
Evidence objects
The research concludes that ARIMA provides the best accuracy, while LightGBM offers a balance of accuracy and computational efficiency. Future work could involve developing ensemble models to further optimize performance. Potential applications include inventory management and sales forecasting for large retail companies.
key_findings bullet 1 · key_findings · validation V0
Raw abstract and provenance
Abstract: Predicting product sales of large retail companies is a challenging task considering volatile nature of trends, seasonalities, events as well as unknown factors such as market competitions, change in customer's preferences, or unforeseen events, e.g., COVID-19 outbreak. In this paper, we benchmark forecasting models on historical sales data from Walmart to predict their future sales. We provide a… ▽ More Predicting product sales of large retail companies is a challenging task considering volatile nature of trends, seasonalities, events as well as unknown factors such as market competitions, change in customer's preferences, or unforeseen events, e.g., COVID-19 outbreak. In this paper, we benchmark forecasting models on historical sales data from Walmart to predict their future sales. We provide a comprehensive theoretical overview and analysis of the state-of-the-art timeseries forecasting models. Then, we apply these models on the forecasting challenge dataset (M5 forecasting by Kaggle). Specifically, we use a traditional model, namely, ARIMA (Autoregressive Integrated Moving Average), and recently developed advanced models e.g., Prophet model developed by Facebook, light gradient boosting machine (LightGBM) model developed by Microsoft and benchmark their performances. Results suggest that ARIMA model outperforms the Facebook Prophet and LightGBM model while the LightGBM model achieves huge computational gain for the large dataset with negligible compromise in the prediction accuracy. △ Less
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