@inproceedings{122483468942401b9d7d60d5585afa37,
title = "Kalman filter based time series prediction of cake factory daily sale",
abstract = "Accurate prediction of future daily sales is a crucial step towards optimal management of daily production of a cake factory. In this study, an interacting multiple model integrated kalman filter was used to predict the future daily sales of cake products. Two years daily sale history of 108 cake products were used to train and test the proposed method. Our experiments show that 1) running interacting multiple models of different orders in parallel is more effective than single classical interacting multiple model; 2) when only daily sale data was used, the proposed method predicted 33.54% of sales within ±10% of true sales; 3) when more variables, including festival and weekend, were combined into the prediction, 34.38% of predicted sales were within ±10% of true sales.",
keywords = "Kalman filter, cake factory, daily sale prediction, interacting multiple models, time series",
author = "Jiaxuan Wu and Qing Fang and Yangying Xu and Jionglong Su and Fei Ma",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2017 ; Conference date: 14-10-2017 Through 16-10-2017",
year = "2017",
month = jul,
day = "2",
doi = "10.1109/CISP-BMEI.2017.8302108",
language = "English",
series = "Proceedings - 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1--7",
editor = "Qingli Li and Lipo Wang and Mei Zhou and Li Sun and Song Qiu and Hongying Liu",
booktitle = "Proceedings - 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2017",
}