A New Price Estimation System for a Large Number of Products in Online Stores (Case Study of Negin Polad Naqsh Jahan Company)

Document Type : Research Paper

Authors

1 Master of computer science, Amirkabir University of Technology

2 Ph.D of Industrial engineering, Negin Poulad Naghshe Jahan company

3 Ph.D of Industrial engineering, Negin Poulad Naghshe Jahan company (Corresponding Author).

4 Master of business management, Negin Poulad Naghshe Jahan company

5 Master of Industrial engineering, Sharif University of Technology

Abstract

Due to the large number of goods provided in online stores, the daily change of goods prices and the impossibility of querying and registering this amount of prices by the operators, it is very necessary to develop a price estimation system for daily forecasting of goods prices with high accuracy. For this purpose, in this article, a new price estimation system was developed using the correlation matrix and linear regression. Due to the high error of linear regression in price prediction, an innovative linear regression method was formulated to increase the prediction accuracy and reduce the average error based on determining the importance of data in terms of time. The developed system was implemented on the real data of Negin Polad Naqsh Jahan Company (Ahanonline.com). The results indicate the high accuracy of the estimated prices by this model. Also, the results of the presented model were examined and analyzed considering the changes in the exchange rate of US Dollar. The results show that, most of the time, the presented heuristic regression has less error than other investigated methods.
 

Keywords

References

gent Business Model for Product Price Prediction Using Machine Learning Approach. Intelligent Automation & Soft Computing, 29(3), 147-159.
Ataman, G., & Kahraman, S. (2021). Stock Market Prediction in Brics Countries Using Linear Regression and Artificial Neural Network Hybrid Models. The Singapore Economic Review, 67(02), 635-653.
Azadeh, A. , Khakestani, M. and M. Saberi (2009), “A Flexible Fuzzy Regression Algorithm for Forecasting Oil Consumption Estimation”, Energy Policy, No. 37, pp. 5567-5579.
Budiantara, M., Gunawan, H., & Utami, E. S. (2019). Perceived Usefulness, Perceived Ease of Use, Trust in Online Store, Perceived Risk Sebagai Pemicu Niat Beli Online Pada Produk Umkm “Made in Indonesia” Melalui Penggunaan E-Commerce Marketplace. Jurnal Riset Akuntansi Mercu Buana, 5(1), 19.
Bujang, M., & Baharum, N. (2016). Sample Size Guideline for Correlation Analysis. World Journal Of Social Science Research, 3(1), 37.
Chaffey, D. (2022). E-Business and E-Commerce Management: Strategy, Implementation and Practice (5th Edition) (5th (Fifth) Edition). Prentice Hall.
Chau, K., Ma, V., & Ho, D. (2001). The Pricing of ‘Luckiness’ in the Apartment Market. Journal of Real Estate Literature, 9(1), 29-40.
Chen, C., Kim, J. B., & Yao, L. (2017). Earnings smoothing: Does it Exacerbate or Constrain Stock Price Crash Risk? Journal of Corporate Finance, 42, 36-54.
Chen, W., Zhang, H., Mehlawat, M. K., & Jia, L. (2021). Mean-Variance Portfolio Optimization Using Machine Learning-Based Stock Price Prediction. Applied Soft Computing, 100, 106943.
Ejdys, J., Ginevicius, R., Rozsa, Z., & Janoskova, K. (2019). The Role of Perceived Risk and Security Level in Building Trust in E-Government Solutions. E+M Ekonomie a Management, 22(3), 220-235.
Emioma, C. C., & Edeki, S. O. (2021). Stock Price Prediction Using Machine Learning on Least-Squares Linear Regression Basis. Journal of Physics: Conference Series, 1734(1), 012058.
Gujarati D.N. Basic Econometrics, 3rd Edition, Mc Graw-Hill International Edition 1995.
Hayton, J. W., & Blundell, M. (2021). Exploring the Relationship Between Social Class and Sport Event Volunteering. Sport Management Review, 24(1), 92-115.
Kedem B., Fokianos K. Regression Model for Time Series Analysis”. Journal of Amerian Statistical Association 2004; 99: 299.
Khashei, M., Reza Hejazi, S., & Bijari, M. (2008). A New Hybrid Artificial Neural Networks and Fuzzy Regression Model for Time Series Forecasting. Fuzzy Sets and Systems, 159(7), 769-786.
Lind D.A., Marchal W.G., Wathen S.A. Statistical Techniques in Business and Economics, 12th edition, New York, Mcgraw-Hill 2005.
Meng, Q.N., Xu, X. (2018). Price Forecasting Using an ACO-Based Support Vector Regression Ensemble in Cloud Manufacturing. Computers & Industrial Engineering, 125, 171-177.
Moskowitch, H, K. J, Kim(1993) «On Assessing the H-Value in Fuzzy Linear Regression», Journal of Fuzzy Sets and Systems, 58, 1993, Pp: 313-320.
Park, J., & Chung, H. (2009). Consumers’ Travel Website Transferring Behaviour: Analysis Using Clickstream Data-Time, Frequency, and Spending. The Service Industries Journal, 29(10), 1451-1463.
Riantini, R. E., Andini, S., Florencia, M. M., & Rabiah, A. S. (2019). E-Marketing Strategy Analysis of Consumer Purchase Decision in Indonesia Online Sports Stores. 2019 International Conference on Information Management and Technology (ICIMTech).
Seitz, C., Pokrivčák, J., Tóth, M., & Plevný, M. (2017). Online Grocery Retailing in Germany: an Explorative Analysis. Journal of Business Economics and Management, 18(6), 1243-1263.
Tseng, F. M., & Lin, L. (2005). A Quadratic Interval Logit Model for Forecasting Bankruptcy. Omega, 33(1), 85-91.
Tseng, F. M., Tzeng, G. H., Yu, H. C., & Yuan, B. J. (2001). Fuzzy ARIMA Model for Forecasting the Foreign Exchange Market. Fuzzy Sets and Systems, 118(1), 9-19.
Wen, H. Z., Sheng-hua, J., & Xiao-yu, G. (2005). Hedonic Price Analysis of Urban Housing: An Empirical Research on Hangzhou, China. Journal of Zhejiang University-SCIENCE A, 6(8), 907-914.
Whitehill, S. (2009). An Introduction to Pricing Correlation Products Using a Pair-Wise Correlation Matrix. The Journal of Credit Risk, 5(1), 97-110.
Witten, I. H., & Frank, E. (2002). Data Mining. Acm Sigmod Record, 31(1), 76-77.
Wu, B., & Tseng, N. F. (2002). A New Approach to Fuzzy Regression Models with Application to Business Cycle Analysis. Fuzzy Sets and Systems, 130(1), 33-42.
Zhang, H., Nguyen, H., Vu, D. A., Bui, X. N., & Pradhan, B. (2021). Forecasting Monthly Copper Price: A Comparative Study of Various Machine Learning-Based Methods. Resources Policy, 73, 102189.
 
 

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History

  • Receive Date: 14 September 2022
  • Revise Date: 11 October 2022
  • Accept Date: 19 October 2022

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