Particle Filter Based Time Series Prediction of Daily Sales of an Online Retailer

Accurate prediction of sales is instrumental to successful management in the industries. It is crucial in formulating business strategies under uncertainties. In this paper, we consider time series in which observations are arriving sequentially. An online time series model integrating with particle filter is used for predicting sales of 80 products in a local online retailer over 400 days. We embed an Autoregressive model into a state space model and carry out time series prediction for all 80 products using a particular Particle Filter called the Sampling Importance Resampling Filter. Our experiment shows that the proposed model successfully predicts 27.5% of sales fluctuating within 10% of the true values. Furthermore, it outperforms the traditional Autoregressive Integrated Moving Average model by 5% for the same metric used.