Price prediction of e-commerce products through Internet sentiment analysis

1. Li, Z., & Li, Z. (2011). 2020 Internet data will be 44 times the current. Information System Engineering, 6, 11.

   Google Scholar 

2. Lagarto, J., De Sousa, J., Martins, A., & Ferrão, P. (2012). Price forecasting in the day-ahead Iberian electricity market using a conjectural variations ARIMA model. In 2012 9th International conference on the European Energy Market (EEM) (pp. 1–7). IEEE.

3. Hsu, C. C., & Chen, C. Y. (2003). Applications of improved grey prediction model for power demand forecasting. Energy Conversion and Management, 44(02), 2241–2249.

   Article  Google Scholar 

4. González, A. M., Roque, A. M. S., & García-González, J. (2005). Modeling and forecasting electricity prices with input/output hidden Markov models. IEEE Transactions on Power Systems, 20(1), 13–24.

   Article  Google Scholar 

5. Yousefi, S., Weinreich, I., & Reinarz, D. (2005). Wavelet-based prediction of oil prices. Chaos, Solitons and Fractals, 25(2), 265–275.

   Article  Google Scholar 

6. Lippmann, R. P. (1987). An introduction to computing with neural nets. IEEE Assp Magazine, 4(2), 4–22.

   Article  Google Scholar 

7. Kimoto, T., Asakawa, K., Yoda, M., et al. (1990). Stock market prediction system with modular neural network. In 1990 IJCNN international joint conference on neural networks (vol. 1, pp. 1–6). IEEE.

8. Lawrence, R. (1998). Using neural networks to forecast stock market prices. Manitoba: University of Manitoba.

   Google Scholar 

9. Mombeini, H., & Yazdani-Chamzini, A. (2015). Modeling gold price via artificial neural network. Journal of Economics, Business and Management, 3(7), 699–703.

   Article  Google Scholar 

10. Liu, Z., Wang, Y., Zhu, S., Zhang, B., & Wei, L. (2015). Steel Prices Index Prediction in China based on BP neural network. In Z. Zhang, Z. Shen, J. Zhang & R. Zhang (Eds.), LISS 2014 (pp. 603–608). Berlin: Springer.

11. Li, Z., & Li, G. (2010). Establishment of eggs market price short-term prediction model. Food and Nutrition in China, 6, 36–40.

    Google Scholar 

12. Xiao, L., & Zhong, W. (2009). Predictive analysis of China’s oil price based upon ARIMA model. Journal of Nanjing University of Aeronautics Astronautics (Social Sciences), 11(4), 41–46.

    Google Scholar 

13. Li, G., Xu, S., Li, Z., & Dong, X. (2010). Study on super short-term forecasting for market price of agro-products—Based on modern times series modeling of daily wholesale price of tomatoes. Journal of Huazhong Agricultural University (Social Sciences Edition), 6, 40–45.

    Google Scholar 

14. Ji, J. (2008). Application of support vector machine in prediction of Consumer Price Index. Modern Computer, 6, 64–66.

    Google Scholar 

15. Wu, J. (2010). Price tendency prediction and countermeasures in Guangxi from 2010 to 2011. Academic Forum, 33(3), 100–103.

    Google Scholar 

16. Zhang, D., & Ren, X. (2014). Analysis on the characteristics and influencing factors of price fluctuation of agricultural products in China: Based on time series decomposition and VAR model. Price Theory and Practice, 9, 69–71.

    Google Scholar 

17. Zhao, C. L., & Wang, B. (2014). Forecasting crude oil price with an autoregressive integrated moving average (ARIMA) model. In Fuzzy information and engineering and operations research and management (pp. 275–286). Berlin: Springer.

18. Mustaffa, Z., Yusof, Y., & Kamaruddin, S. S. (2014). Enhanced Artificial Bee Colony for training least squares support vector machines in commodity price forecasting. Journal of Computational Science, 5(2), 196–205.

    Article  Google Scholar 

19. Zou, K., Li, B., & Zhou, X. (2009). Application of grey system model in stock prediction. In B. Cao, T. F. Li & C. Y. Zhang (Eds.), Fuzzy information and engineering volume 2: Advance in intelligent and soft computing (vol. 62, pp. 1561–1567). Berlin: Springer.

20. Hang, T. N., & Nabney, I. T. (2010). Short-term electricity demand and gas price forecasts using wavelet transforms and adaptive models. Energy, 35(9), 3674–3685.

    Article  Google Scholar 

21. Pai, P. F., & Lin, C. S. (2005). A hybrid ARIMA and support vector machines model in stock price forecasting. Omega, 33(6), 497–505.

    Article  Google Scholar 

22. Tseng, F. M., Yu, H. C., & Tzeng, G. H. (2001). Combining neural network model with seasonal time series ARIMA model. Technological Forecasting and Social Change, 69(1), 71–87.

    Article  Google Scholar 

23. Janoski, T., & Isaac, L. W. (1994). Introduction to time-series analysis. Comparative Political Economy of the Welfare State, 90(1), 110.

    Google Scholar 

24. Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297.

    Google Scholar 

25. McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. The Bulletin of Mathematical Biophysics, 5(4), 115–133.

    Article  Google Scholar 

26. Huiwen, W., & Jie, M. (2007). Predictive modeling on multivariate linear regression. Journal of Beijing University of Aeronautics and Astronautics, 33(4), 500–504.

    Google Scholar 

27. Wei, B. (2004). Nonlinear regression model. In Proceedings of the ninth annual academic conference of Jiangsu provincial Statistical Research Association.

28. Yong, Z., & Xiaowo, T. (1995). Forecasting with general logistic curves. Application of Statistics and Management, 1, 41–46.

    Google Scholar 

29. Jank, W., Shmueli, G., & Wang, S. (2006). Dynamic, real-time forecasting of online auctions via functional models. In Acm sigkdd international conference on knowledge discovery and data mining (pp. 580–585).

30. Enders, B. W. (2010). Applied econometric time series, 3rd Edition[J].

31. Freeman, J. R. (1983). Granger causality and time series analysis of political relationships[J]. American Journal of Political Science, 27(2), 327–358.

    Article  Google Scholar 

32. Özdemir, D. (2016). The analysis of time series. In Applied statistics for economics and business (pp. 257–291). Cham: Springer.

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