A comparative Time Series analysis of the different categories of items based on holidays and other events

Daily retail sales are impacted by a lot of external factors, holidays and special events are one such category. In general, retail sales are largely impacted by fluctuations in demand hence, it is common for a retailer to run out of stock for some items and overstock the other items and this happens due to the lack of understanding of the actual number of items in demand for a particular item at a particular time of the month. This research work examines the impact of holidays or special events on the sales of a wide category of items using predictive analytics. It is done by performing exploratory data analysis and pre-processing methods followed by feature engineering and information extraction to extract the optimal input parameters to be fed into the model. The dataset is time-series data however, advanced machine learning algorithms are also used along with time-series methods to see if time-series data works well with non-time-series algorithms. Different time-series methods along with gradient boosting and the Facebook prophet model are evaluated in this work, achieving 92.83 % forecast accuracy with the Facebook prophet model. The gradient boosting model performs well with a MAPE value of 22.25% and time-series Holt Winters' additive method provides a MAPE value of 12.84 %. Each of the algorithms provides a good score with this time-series data and an appropriate algorithm can be chosen as per the business need.