@inproceedings{d0807cf8fe614ac4b74b6ead7cf13060,
title = "Particle Filter Based Time Series Prediction of Daily Sales of an Online Retailer",
abstract = "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.",
keywords = "autoregressive integrated moving average model, autoregressive model, particle filter, sales prediction, time series",
author = "Xueye Ping and Qinyi Chen and Guoquan Liu and Jionglong Su and Fei Ma",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2018 ; Conference date: 13-10-2018 Through 15-10-2018",
year = "2018",
month = jul,
day = "2",
doi = "10.1109/CISP-BMEI.2018.8633040",
language = "English",
series = "Proceedings - 2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "Wei Li and Qingli Li and Lipo Wang",
booktitle = "Proceedings - 2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2018",
}