[1] LIU H, LONG Z. An improved deep learning model for predicting stock market price time series[J]. Digital Signal Processing, 2020, 102: 102741.
[2] MOKNI K. A dynamic quantile regression model for the relationship between oil price and stock markets in oil-importing and oil-exporting countries[J]. Energy, 2020, 213: 118639.
[3] WANG L, MA F, LIU J, et al. Forecasting stock price volatility: new evidence from the GARCH-MIDAS model[J]. International Journal of Forecasting, 2020, 36(2): 684-694.
[4] OLANIYI S A S, ADEWOLE K S, JIMOH R G. Stock trend prediction using regression analysis-a data mining approach[J]. ARPN Journal of Systems and Software, 2011, 1(4): 154-157.
[5] FRANSES P H, GHIJSELS H. Additive outliers, GARCH and forecasting volatility[J]. International Journal of Forecasting, 1999, 15(1): 1-9.
[6] MONDAL P, SHIT L, GOSWAMI S. Study of effectiveness of time series modeling (ARIMA) in forecasting stock prices[J]. International Journal of Computer Science, Engineering and Applications, 2014, 4(2): 13.
[7] CHALLA M L, MALEPATI V, KOLUSU S N R. S&p bse sensex and s&p bse it return forecasting using arima[J]. Financial Innovation, 2020, 6(1): 1-19.
[8] LONG J, CHEN Z, HE W, et al. An integrated framework of deep learning and knowledge graph for prediction of stock price trend: an application in Chinese stock exchange market[J]. Applied Soft Computing, 2020, 91: 106205.
[9] CHEN Y, WU J, WU Z. China’s commercial bank stock price prediction using a novel K-means-LSTM hybrid approach[J]. Expert Systems with Applications, 2022, 202: 117370.
[10] TAY F E H, CAO L. Application of support vector machines in financial time series forecasting[J]. Omega, 2001, 29(4): 309-317.
[11] BISHOP C M. Neural networks and their applications[J]. Review of Scientific Instruments, 1994, 65(6): 1803-1832.
[12] YU Z, QIN L, CHEN Y, et al. Stock price forecasting based on LLE-BP neural network model[J]. Physica A: Statistical Mechanics and Its Applications, 2020, 553: 124197.
[13] LIANG Y, LIN Y, LU Q. Forecasting gold price using a novel hybrid model with ICEEMDAN and LSTM-CNN-CBAM[J]. Expert Systems with Applications, 2022, 206: 117847.
[14] CAO J, WANG J. Stock price forecasting model based on modified convolution neural network and financial time series analysis[J]. International Journal of Communication Systems, 2019, 32(12): e3987.
[15] SHERSTINSKY A. Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network[J]. Physica D: Nonlinear Phenomena, 2020, 404: 132306.
[16] YU Y, SI X, HU C, et al. A review of recurrent neural networks: LSTM cells and network architectures[J]. Neural Computation, 2019, 31(7): 1235-1270.
[17] XU G, MENG Y, QIU X, et al. Sentiment analysis of comment texts based on BiLSTM[J]. IEEE Access, 2019, 7: 51522-51532.
[18] SIAMI-NAMINI S, TAVAKOLI N, NAMIN A S. The performance of LSTM and BiLSTM in forecasting time series[C]//2019 IEEE International Conference on Big Data, 2019: 3285-3292.
[19] PIRANI M, THAKKAR P, JIVRANI P, et al. A comparative analysis of ARIMA, GRU, LSTM and BiLSTM on financial time series forecasting[C]//2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE), 2022: 1-6.
[20] ZANG H, XU R, CHENG L, et al. Residential load forecasting based on LSTM fusing self-attention mechanism with pooling[J]. Energy, 2021, 229: 120682.
[21] CHENG W, WANG Y, PENG Z, et al. High-efficiency chaotic time series prediction based on time convolution neural network[J]. Chaos, Solitons & Fractals, 2021, 152: 111304.
[22] LI J, LIU Y, LI Q. Intelligent fault diagnosis of rolling bearings under imbalanced data conditions using attention-based deep learning method[J]. Measurement, 2022, 189: 110500.
[23] SONG S, YANG Z, GOH H H, et al. A novel sky image-based solar irradiance nowcasting model with convolutional block attention mechanism[J]. Energy Reports, 2022, 8: 125-132.
[24] LI D, LIU J, ZHAO Y. Prediction of multi-site PM2.5 concentrations in Beijing using CNN-BiLSTM with CBAM[J]. Atmosphere, 2022, 13(10): 1719.
[25] DENG C, HUANG Y, HASAN N, et al. Multi-step-ahead stock price index forecasting using long short-term memory model with multivariate empirical mode decomposition[J]. Information Sciences, 2022, 607: 297-321.
[26] DRAGOMIRETSKIY K, ZOSSO D. Variational mode decomposition[J]. IEEE Transactions on Signal Processing, 2013, 62(3): 531-544.
[27] MA X, LIN Z, JIAO L, et al. Phase identification with VMD and HT combined method for an active seismic source experiment[J]. Measurement, 2022, 201: 111689.
[28] NIU H, XU K, WANG W. A hybrid stock price index forecasting model based on variational mode decomposition and LSTM network[J]. Applied Intelligence, 2020, 50: 4296-4309.
[29] YU L, WANG Z, TANG L. A decomposition-ensemble model with data-characteristic-driven reconstruction for crude oil price forecasting[J]. Applied Energy, 2015, 156: 251-267.
[30] ZHOU F, ZHOU H, YANG Z, et al. EMD2FNN: a strategy combining empirical mode decomposition and factorization machine based neural network for stock market trend prediction[J]. Expert Systems with Applications, 2019, 115: 136-151.
[31] HANSEN P R, LUNDE A, NASON J M. The model confidence set[J]. Econometrica, 2011, 79(2): 453-497.
[32] WOO S, PARK J, LEE J Y, et al. Cbam: convolutional block attention module[C]//Proceedings of the European Conference on Computer Vision (ECCV), 2018: 3-19.
[33] GREFF K, SRIVASTAVA R K, KOUTNIK J, et al. LSTM: a search space odyssey[J]. IEEE Transactions on Neural Networks and Learning Systems, 2016, 28(10): 2222-2232.
[34] HUANG C G, HUANG H Z, LI Y F. A bidirectional LSTM prognostics method under multiple operational conditions[J]. IEEE Transactions on Industrial Electronics, 2019, 66(11): 8792-8802.
[35] BENGIO Y, COURVILLE A, VINCENT P. Representation learning: a review and new perspectives[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(8): 1798-1828.
[36] NELSON D M Q, PEREIRA A C M, DE OLIVEIRA R A. Stock market’s price movement prediction with LSTM neural networks[C]//2017 International Joint Conference on Neural Networks (IJCNN), 2017: 1419-1426.
[37] SELVIN S, VINAYAKUMAR R, GOPALAKRISHNAN E A, et al. Stock price prediction using LSTM, RNN and CNN-sliding window model[C]//2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2017: 1643-1647.
[38] REZAEI H, FAALJOU H, MANSOURFAR G. Stock price prediction using deep learning and frequency decomposition[J]. Expert Systems with Applications, 2021, 169: 114332.
[39] GAO Z, ZHANG J. The fluctuation correlation between investor sentiment and stock index using VMD-LSTM: evidence from China stock market[J]. The North American Journal of Economics and Finance, 2023, 66: 101915.
[40] LI X, WEI Y. The dependence and risk spillover between crude oil market and China stock market: new evidence from a variational mode decomposition-based copula method[J]. Energy Economics, 2018, 74: 565-581.
[41] WANG W, LIN W, WEN Y, et al. An interpretable intuitionistic fuzzy inference model for stock prediction[J]. Expert Systems with Applications, 2023, 213: 118908.
[42] DESSAIN J. Machine learning models predicting returns: why most popular performance metrics are misleading and proposal for an efficient metric[J]. Expert Systems with Applications, 2022, 199: 116970.
[43] REHMAN N U, AFTAB H. Multivariate variational mode decomposition[J]. IEEE Transactions on Signal Processing, 2019, 67(23): 6039-6052. |