  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.
