TY - GEN
T1 - Online Shop Daily Sale Prediction Using Adaptive Network-Based Fuzzy Inference System
AU - Liang, Yuanbang
AU - Jia, Yunyu
AU - Li, Jinglin
AU - Chen, Meiyi
AU - Hu, Yifan
AU - Shi, Yinan
AU - Ma, Fei
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - Online shopping is an increasingly popular way of purchasing among customers. Demands from online shopping are usually highly dynamic. Inventory management, to many online shop owners, hence is a challenging task. Accurate forecasting of daily sales helps better replenishment and sale. This paper proposes a method for online shop daily sale forecasting. In the method, Kalman Filter was firstly applied on the historic sale data to smooth the data. Adaptive Network-based Fuzzy Inference System (ANFIS) was then built to achieve time series forecasting. Sale histories of an online shop was used to evaluate the method. Overall performance of ANFIS was evaluated with MAPE and ACC({pm}), and were found to be 1.0725 and 40%, respectively. The results were found to be slightly better than Back Propagation Neural Network which has MAPE 4.9891 and ACC({\pm}) 39.85{\%}. 200 products were divided into 6 groups based on corresponding MAPE and ACC({\pm}). The results showed that daily sales of 22.86% products were predicted accurately, while that of 34.29% products were badly predicted. The results also indicated that membership function inserted in the model causes little impact on prediction accuracy. It was tested that the average performance indicator ACC({\pm}) ({\%}) stays stable with different membership functions.
AB - Online shopping is an increasingly popular way of purchasing among customers. Demands from online shopping are usually highly dynamic. Inventory management, to many online shop owners, hence is a challenging task. Accurate forecasting of daily sales helps better replenishment and sale. This paper proposes a method for online shop daily sale forecasting. In the method, Kalman Filter was firstly applied on the historic sale data to smooth the data. Adaptive Network-based Fuzzy Inference System (ANFIS) was then built to achieve time series forecasting. Sale histories of an online shop was used to evaluate the method. Overall performance of ANFIS was evaluated with MAPE and ACC({pm}), and were found to be 1.0725 and 40%, respectively. The results were found to be slightly better than Back Propagation Neural Network which has MAPE 4.9891 and ACC({\pm}) 39.85{\%}. 200 products were divided into 6 groups based on corresponding MAPE and ACC({\pm}). The results showed that daily sales of 22.86% products were predicted accurately, while that of 34.29% products were badly predicted. The results also indicated that membership function inserted in the model causes little impact on prediction accuracy. It was tested that the average performance indicator ACC({\pm}) ({\%}) stays stable with different membership functions.
KW - Adaptive Network
KW - Daily sale prediction
KW - Fuzzy Inference System
KW - Kalman Filter
KW - Online shop
KW - Time Series Prediction
UR - http://www.scopus.com/inward/record.url?scp=85079174408&partnerID=8YFLogxK
U2 - 10.1109/CISP-BMEI48845.2019.8966058
DO - 10.1109/CISP-BMEI48845.2019.8966058
M3 - Conference Proceeding
AN - SCOPUS:85079174408
T3 - Proceedings - 2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2019
BT - Proceedings - 2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2019
A2 - Li, Qingli
A2 - Wang, Lipo
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2019
Y2 - 19 October 2019 through 21 October 2019
ER -