E3S Web Conf.
Volume 235, 20212020 International Conference on New Energy Technology and Industrial Development (NETID 2020)
|Number of page(s)||6|
|Section||Analysis on the Development of Intelligent Supply Chain and Internet Digital Industrialization|
|Published online||03 February 2021|
- Fethi M D, Pasiouras F. Assessing bank efficiency and performance with operational research and artificial intelligence techniques: A survey[J]. European journal of operational research, 2010, 204(2): 189-198. [Google Scholar]
- Applications of artificial intelligence in finance and economics[M]. Emerald Group Publishing Limited, 2004. [Google Scholar]
- Krollner B, Vanstone B J, Finnie G R. Financial time series forecasting with machine learning techniques: a survey[C]//ESANN. 2010. [Google Scholar]
- Trippi R R, Turban E. Neural networks in finance and investing: Using artificial intelligence to improve real world performance[M]. McGraw-Hill, Inc., 1992. [Google Scholar]
- Bahrammirzaee A. A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems[J]. Neural Computing and Applications, 2010, 19(8): 1165-1195. [Google Scholar]
- Trippi R R, By-Lee P, Jae K. Artificial intelligence in finance and investing: state-of-the-art technologies for securities selection and portfolio management[M]. McGraw-Hill, Inc., 1995. [Google Scholar]
- Kim K. Artificial neural networks with evolutionary instance selection for financial forecasting[J]. Expert Systems with Applications, 2006, 30(3): 519-526. [Google Scholar]
- Liu Y, Chen Y, Wu S, et al. Composite leading search index: a preprocessing method of internet search data for stock trends prediction[J]. Annals of Operations Research, 2015, 234(1):77-94. [Google Scholar]
- Qian B, Rasheed K. Stock market prediction with multiple classifiers[J]. Applied Intelligence: The International Journal of Artificial, Intelligence, Neural Networks, and Complex Problem-Solving Technologies, 2007, 26(1):25-33. [Google Scholar]
- Chen M Y, Chen B T. A hybrid fuzzy time series model based on granular computing for stock price forecasting[J]. Information Sciences, 2015, 294(2):227-241. [Google Scholar]
- Kao L J, Chiu C C, Lu C J, et al. A hybrid approach by integrating wavelet-based feature extraction with MARS and SVR for stock index forecasting[J]. Decision Support Systems, 2013, 54(3):1228-1244. [Google Scholar]
- Singh P, Borah B. Forecasting stock index price based on M-factors fuzzy time series and particle swarm optimization[J]. International Journal of Approximate Reasoning, 2014, 55(3):812-833. [Google Scholar]
- Lee B J, Kim K H, Ku B, et al. Prediction of body mass index status from voice signals based on machine learning for automated medical applications[J]. Artificial Intelligence in Medicine, 2013, 58(1):51-61. [PubMed] [Google Scholar]
- Humphries G R W, Bragg C, Overton J, et al. Pattern recognition in long-term Sooty Shearwater data: applying machine learning to create a harvest index[J]. Ecological Applications, 2014, 24(8):2107-2121. [Google Scholar]
- Deo R C, Şahin Mehmet. Application of the extreme learning machine algorithm for the prediction of monthly Effective Drought Index in eastern Australia[J]. Atmospheric Research, 2015, 153:512-525. [Google Scholar]
- Patel J, Shah S, Thakkar P, et al. Predicting stock and stock price index movement using Trend Deterministic Data Preparation and machine learning techniques[J]. Expert Systems with Applications, 2015, 42(1):259-268. [Google Scholar]
- Araújo M, Matías J. M, Rivas T, et al. Machine learning techniques applied to the construction of a new geomechanical quality index[J]. International Journal of Computer Mathematics, 2011, 88(9):1830-1838. [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.