Prediction of online trade growth using search-ANFIS: Transactions on Taobao as examples

Author

Wang Jiyuan ; Peng Geng ; Dai Wei

Author_Institution

Sch. of Manage., Univ. of Chinese Acad. of Sci., Beijing, China

fYear

2014

fDate

6-11 July 2014

Firstpage

2566

Lastpage

2571

Abstract

The growth of E-commerce which can be seen in recent years, has contributed a lot to global economy. Prediction of trade, especially in C2C market, can help decision-makers obtain the information from the online transactions and find the knowledge underlying the data. This paper facilities the traditional search index prediction system with ANFIS model. By using purchasing transactions from Taobao, a C2C company in China, this paper trains and tests the model. Results show that, compared with traditional regression analysis method, Search-ANFIS system has higher prediction accuracy in online trade prediction.

Keywords

electronic commerce; fuzzy neural nets; fuzzy reasoning; purchasing; search problems; C2C market; Taobao; decision-makers; e-commerce; global economy; online trade growth prediction; online transactions; purchasing transactions; regression analysis method; search index prediction system; search-ANFIS model; Accuracy; Adaptation models; Benchmark testing; Data models; Indexes; Predictive models; Training;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems (FUZZ-IEEE), 2014 IEEE International Conference on

Conference_Location

Beijing

Print_ISBN

978-1-4799-2073-0

Type

conf

DOI

10.1109/FUZZ-IEEE.2014.6891527

Filename

6891527