Open Access
Issue |
E3S Web Conf.
Volume 218, 2020
2020 International Symposium on Energy, Environmental Science and Engineering (ISEESE 2020)
|
|
---|---|---|
Article Number | 01026 | |
Number of page(s) | 5 | |
Section | Research on Energy Technology Application and Consumption Structure | |
DOI | https://doi.org/10.1051/e3sconf/202021801026 | |
Published online | 11 December 2020 |
- G. Tabachnick and L. S. Fidell. (2001). Using Multivariate Statistics, Pearson Education, Upper Saddle River, NJ, USA, 4th edition. [Google Scholar]
- A. Meyler, G. Kenny, and T. Quinn. (1998). Forecasting Irish Inflation Using ARIMA Models. Technical Paper 3/RT/1998, Central Bank of Ireland Research Department. [Google Scholar]
- M. Khashei and M. Bijari. (2010). An artificial neural network (p, d, q) model for timeseries forecasting. Expert Systems with Applications, vol. 37, no. 1, pp. 479–489. [CrossRef] [Google Scholar]
- Y. Chen, B. Yang, J. Dong, and A. Abraham. (2005). Time-series forecasting using flexible neural tree model. Information Sciences, vol. 174, no. 3-4, pp. 219–235. [CrossRef] [Google Scholar]
- F. Giordano, M. La Rocca, and C. Perna. (2007). Forecasting nonlinear time series with neural network sieve bootstrap. Computational Statistics and Data Analysis, vol. 51, no. 8, pp. 3871–3884. [CrossRef] [Google Scholar]
- A. Jain and A. M. Kumar. (2007). Hybrid neural network models for hydrologic time series forecasting. Applied Soft Computing Journal, vol. 7, no. 2, pp. 585–592. [CrossRef] [Google Scholar]
- A. Sethia, P. Raut. (2018). Application of LSTM, GRU and ICA for Stock Price Prediction. Proceedings of ICTIS 2018, Volume 2. [Google Scholar]
- G. Zhang, B. E. Patuwo and M. Y. Hu. (1998). Forecasting with artificial neural networks: The state of the art. International Journal of Forecasting, 14, 35–62. [CrossRef] [Google Scholar]
- P. G. Benardos and G. C. Vosniakos. (2002). Prediction of surface roughness in CNC face milling using neural networks and Taguchi’s design of experiments. Robotics and Computer Integrated Manufacturing, 18, 43–354. [CrossRef] [Google Scholar]
- J. Leski and E. Czogala. (1999). A new artificial network based fuzzy interference system with moving consequents in if–then rules and selected applications. Fuzzy Sets and Systems, 108, 289–297. [CrossRef] [Google Scholar]
- Jiang, X., & Wah, A. H. K. S. (2003). Constructing and training feed-forward neural networks for pattern classification. Pattern Recognition, 36, 853–867. [CrossRef] [Google Scholar]
- A. A. Adebiyi, A. O. Adewumi, and C. K. Ayo. (2014). Comparison of ARIMA and Artificial Neural Networks Models for Stock Price Prediction. Journal of Applied Mathematics, Volume 2014. [Google Scholar]
- J. T. Yao, C. L. Tan, and H. L. Poh. (1999). Neural networks for technical analysis: a study on KLCI. International Journal of Theoretical and Applied Finance, vol. 2, no. 2, pp. 221–241. [CrossRef] [Google Scholar]
- V. R. Prybutok, J. Yi, and D. Mitchell. (2000). Comparison of neural network models with ARIMA and regression models for prediction of Houston’s daily maximum ozone concentrations. European Journal of Operational Research, vol. 122, no. 1, pp. 31–40. [CrossRef] [Google Scholar]
- Tang, Him et al. (2003). Finite Mixture of ARMA-GARCH Model for Stock Price Prediction. CIEF, 2003, pp. 1112-1119. [Google Scholar]
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