AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |
Back to Blog
Vmd stock nyse1/20/2024 ![]() Li GH, Zheng CF, Yang H (2022) Carbon price combination prediction model based on improved variational mode decomposition. Jujie WANG, Chunchen FENG, Junjie HE, Liu FENG, Yang LI (2020) A novel multi-factor stock index prediction approach using principal component analysis feature classification and two-stage long shortterm memory network with residual correction. Joo YC, Park SY (2021) The impact of oil price volatility on stock markets: evidences from oil-importing countries. Ji Y, Liew AWC, Yang LX (2021) A novel improved particle swarm optimization with long-short term memory hybrid model for stock indices forecast. ![]() Holland JH (1992) Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Henrique BM, Sobreiro VA, Kimura H (2019) Literature review: machine learning techniques applied to financial market prediction. Guegan D (2009) Chaos in economics and finance. įischer T, Krauss C (2018) Deep learning with long short-term memory networks for financial market predictions. įang JC, Gozgor G, Lau CKM, Lu Z (2020) The impact of Baidu index sentiment on the volatility of China’s stock markets. Doi: Ĭhung H, Shin KS (2018) Genetic algorithm-optimized long short-term memory network for stock market prediction. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Ĭhen TQ, Guestrin C (2016) XGBoost: A Scalable Tree Boosting System. Ĭhen YJ, Hao YJ (2018) Integrating principle component analysis and weighted support vector machine for stock trading signals prediction. Ĭhen SS (2011) Lack of consumer confidence and stock returns. Ĭhandar SK (2021) Hybrid models for intraday stock price forecasting based on artificial neural networks and metaheuristic algorithms. Ĭao W, Zhu WD, Wang WJ, Demazeau Y, Zhang C (2020) A deep coupled LSTM approach for USD/CNY exchange rate forecasting. Ĭao J, Li Z, Li J (2019) Financial time series forecasting model based on CEEMDAN and LSTM. (86)90063-1Ĭao GX, Han Y, Li QC, Xu W (2017) Asymmetric MF-DCCA method based on risk conduction and its application in the Chinese and foreign stock markets. īollerslev T (1986) Generalized autoregressive conditional heteroskedasticity. īao W, Yue J, Rao YL (2017) A deep learning framework for financial time series using stacked autoencoders and long-short term memory. National Association of Securities Dealers Automated Quotations NYSE:Ĭhicago Board Options Exchange Volatility Index XGBoost:Ībbas G, Hammoudeh S, Shahzad SJH, Wang SY, Weie YJ (2019) Return and volatility connectedness between stock markets and macroeconomic factors in the G-7 countries. Mean square percentage error MSPE NASDAQ: ![]() The number of neurons in each layer LSTM: At the same time, these results fully prove that the prediction model proposed by us possesses more reliable and better predictive ability.Ĭomplete ensemble empirical mode de- composition with adaptive noise CPI:įinancial Times Stock Exchange 100 Index GA: The experimental results indicate that the performance of the proposed model outperforms other baseline models in China's two stock markets and the New York stock exchange. Finally, the results of each class predicted by the GA-LSTM model are nonlinearly integrated to acquire the final prediction model, which is applied to the prediction of the test set. Further, multiple parameters of long short-term memory (LSTM) are optimized by genetic algorithm (GA), and multiple GA-LSTM models are obtained by training each clustering result. The second step is to apply the idea of classification prediction to cluster the filtered feature set. Firstly, a multi-factor analysis is carried out to select a variety of factors that have an impact on the stock price, and adopt the extreme gradient boosting (XGBoost) algorithm to eliminate factors with low correlation. In order to promote the accuracy of stock price prediction, a multi-factor two-stage deep learning integrated prediction system based on intelligent optimization and feature clustering is proposed to predict stock price in this paper. However, accurate prediction of the stock price is a thorny task, because stock price fluctuations are non-linear and chaotic. Accurate prediction of stock prices plays a decisive role in constructing the investment decision or risk hedging. Stock market fluctuations have a great impact on various economic and financial activities worldwide.
0 Comments
Read More
Leave a Reply. |