Enhancing stock market predictions via hybrid external trend and internal components analysis and long short term memory model
References
[1]
Abdulrahman Umar Farouk Ibn, Ussiph Najim, Hayfron-Acquah Benjamin, A hybrid arima-LSTM model for stock price prediction, Int. J. Comput. Eng. Inf. Technol. 12 (8) (2021) 48–51.
[2]
Ahmadi Elham, Jasemi Milad, Monplaisir Leslie, Nabavi Mohammad Amin, Mahmoodi Armin, Jam Pegah Amini, New efficient hybrid candlestick technical analysis model for stock market timing on the basis of the Support Vector Machine and Heuristic Algorithms of Imperialist Competition and Genetic, Expert Syst. Appl. 94 (2018) 21–31.
[3]
Albeladi Khulood, Zafar Bassam, Mueen Ahmed, Time series forecasting using LSTM and ARIMA, Int. J. Adv. Comput. Sci. Appl. (IJACSA) 14 (1) (2023).
[4]
Ali Muhammad, Khan Dost Muhammad, Alshanbari Huda M., El-Bagoury Abd Al-Aziz Hosni, Prediction of complex stock market data using an improved hybrid emd-lstm model, Appl. Sci. 13 (3) (2023) 1429.
[5]
Argollo de Menezes M., Barabási A.-L., Separating internal and external dynamics of complex systems, Phys. Rev. Lett. 93 (6) (2004).
[6]
Barthélemy Marc, Nadal Jean-Pierre, Berestycki Henri, Disentangling collective trends from local dynamics, Proc. Natl. Acad. Sci. 107 (17) (2010) 7629–7634.
[7]
Bengio Yoshua, Simard Patrice, Frasconi Paolo, Learning long-term dependencies with gradient descent is difficult, IEEE Trans. Neural Netw. 5 (2) (1994) 157–166.
[8]
Bhandari Hum Nath, Rimal Binod, Pokhrel Nawa Raj, Rimal Ramchandra, Dahal Keshab R., Khatri Rajendra K.C., Predicting stock market index using LSTM, Mach. Learn. Appl. 9 (2022).
[9]
Bousoño-Calzón Carlos, Bustarviejo-Muñoz Josué, Aceituno-Aceituno Pedro, Escudero-Garzás José Joaquín, On the economic significance of stock market prediction and the no free lunch theorem, IEEE Access 7 (2019) 75177–75188.
[10]
Brandi Giuseppe, Gramatica Ruggero, Di Matteo Tiziana, Unveil stock correlation via a new tensor-based decomposition method, J. Comput. Sci. 46 (2020).
[11]
Bustos Oscar, Pomares-Quimbaya Alexandra, Stock market movement forecast: A systematic review, Expert Syst. Appl. 156 (2020).
[12]
Cao Jian, Li Zhi, Li Jian, Financial time series forecasting model based on CEEMDAN and LSTM, Phys. A 519 (2019) 127–139.
[13]
Chen Lu, Chi Yonggang, Guan Yingying, Fan Jialin, A hybrid attention-based EMD-LSTM model for financial time series prediction, in: 2019 2nd International Conference on Artificial Intelligence and Big Data, ICAIBD, IEEE, 2019, pp. 113–118.
[14]
Chung Junyoung, Gulcehre Caglar, Cho KyungHyun, Bengio Yoshua, Empirical evaluation of gated recurrent neural networks on sequence modeling, 2014, arXiv preprint arXiv:1412.3555.
[15]
Dave Emmanuel, Leonardo Albert, Jeanice Marethia, Hanafiah Novita, Forecasting Indonesia exports using a hybrid model ARIMA-LSTM, Procedia Comput. Sci. 179 (2021) 480–487.
[16]
Deng Yamin, Fan Huifang, Wu Shiman, A hybrid ARIMA-LSTM model optimized by BP in the forecast of outpatient visits, J. Ambient Intell. Humaniz. Comput. (2023) 1–11.
[17]
Eapen Jithin, Bein Doina, Verma Abhishek, Novel deep learning model with CNN and bi-directional LSTM for improved stock market index prediction, in: 2019 IEEE 9th Annual Computing and Communication Workshop and Conference, CCWC, IEEE, 2019, pp. 0264–0270.
[18]
Fama Eugene F., Random walks in stock market prices, Financ. Anal. J. 51 (1) (1995) 75–80.
[19]
Hajirahimi Zahra, Khashei Mehdi, Hybrid structures in time series modeling and forecasting: A review, Eng. Appl. Artif. Intell. 86 (2019) 83–106.
[20]
Hiransha M.E.A.G., Gopalakrishnan E. Ab, Menon Vijay Krishna, Soman K.P., NSE stock market prediction using deep-learning models, Procedia Comput. Sci. 132 (2018) 1351–1362.
[21]
Hochreiter Sepp, The vanishing gradient problem during learning recurrent neural nets and problem solutions, Internat. J. Uncertain. Fuzziness Knowledge-Based Systems 6 (02) (1998) 107–116.
[22]
Hoseinzade Ehsan, Haratizadeh Saman, Khoeini Arash, U-cnnpred: A universal cnn-based predictor for stock markets, 2019, arXiv preprint arXiv:1911.12540.
[23]
Hu Y., Stock market timing model based on convolutional neural network–a case study of Shanghai composite index, Finance Econ. 4 (2018) 71–74.
[24]
Huang Yusheng, Gao Yelin, Gan Yan, Ye Mao, A new financial data forecasting model using genetic algorithm and long short-term memory network, Neurocomputing 425 (2021) 207–218.
[25]
Jin Zhigang, Yang Yang, Liu Yuhong, Stock closing price prediction based on sentiment analysis and LSTM, Neural Comput. Appl. 32 (2020) 9713–9729.
[26]
Jing Nan, Wu Zhao, Wang Hefei, A hybrid model integrating deep learning with investor sentiment analysis for stock price prediction, Expert Syst. Appl. 178 (2021).
[27]
Jothimani Dhanya, Yadav Surendra S., Stock trading decisions using ensemble-based forecasting models: a study of the Indian stock market, J. Bank. Financ. Technol. 3 (2) (2019) 113–129.
[28]
Kulshreshtha Sakshi, et al., An ARIMA-LSTM hybrid model for stock market prediction using live data, J. Eng. Sci. Technol. Rev. 13 (4) (2020).
[29]
Kumar Deepak, Meghwani Suraj S., Thakur Manoj, Proximal support vector machine based hybrid prediction models for trend forecasting in financial markets, J. Comput. Sci. 17 (2016) 1–13.
[30]
Kumbure Mahinda Mailagaha, Lohrmann Christoph, Luukka Pasi, Porras Jari, Machine learning techniques and data for stock market forecasting: A literature review, Expert Syst. Appl. 197 (2022).
[31]
Kurani Akshit, Doshi Pavan, Vakharia Aarya, Shah Manan, A comprehensive comparative study of artificial neural network (ANN) and support vector machines (SVM) on stock forecasting, Ann. Data Sci. 10 (1) (2023) 183–208.
[32]
Lahmiri Salim, Intraday stock price forecasting based on variational mode decomposition, J. Comput. Sci. 12 (2016) 23–27.
[33]
Le Xuan-Hien, Ho Hung Viet, Lee Giha, Jung Sungho, Application of long short-term memory (LSTM) neural network for flood forecasting, Water 11 (7) (2019) 1387.
[34]
Lee Si Woon, Kim Ha Young, Stock market forecasting with super-high dimensional time-series data using ConvLSTM, trend sampling, and specialized data augmentation, Expert Syst. Appl. 161 (2020).
[35]
Lu Wenjie, Li Jiazheng, Li Yifan, Sun Aijun, Wang Jingyang, A CNN-LSTM-based model to forecast stock prices, Complexity 2020 (2020) 1–10.
[36]
Murphy John J., Technical Analysis of the Financial Markets: A Comprehensive Guide to Trading Methods and Applications, Penguin, 1999.
[37]
Nabipour Mojtaba, Nayyeri Pooyan, Jabani Hamed, Mosavi Amir, Salwana Ely, S Shahab, Deep learning for stock market prediction, Entropy 22 (8) (2020) 840.
[38]
Niu Hongli, Xu Kunliang, Wang Weiqing, A hybrid stock price index forecasting model based on variational mode decomposition and LSTM network, Appl. Intell. 50 (2020) 4296–4309.
[39]
Rehman Ziad Ur, Boulaaras Salah, Jan Rashid, Ahmad Imtiaz, Bahramand Salma, Computational analysis of financial system through non-integer derivative, J. Comput. Sci. 75 (2024).
[40]
Rezaei Hadi, Faaljou Hamidreza, Mansourfar Gholamreza, Stock price prediction using deep learning and frequency decomposition, Expert Syst. Appl. 169 (2021).
[41]
Temur Ayse Soy, Akgün Melek, Temur Gunay, Predicting housing sales in Turkey using ARIMA, LSTM and hybrid models, J. Bus. Econ. Manag. 20 (5) (2019) 920–938.
[42]
Xuan Yuze, Yu Yue, Wu Kaisu, Prediction of short-term stock prices based on EMD-LSTM-CSI neural network method, in: 2020 5th IEEE International Conference on Big Data Analytics, ICBDA, IEEE, 2020, pp. 135–139.
[43]
Yan Binbin, Aasma Memon, et al., A novel deep learning framework: Prediction and analysis of financial time series using CEEMD and LSTM, Expert Syst. Appl. 159 (2020).
[44]
Zolfaghari Mehdi, Gholami Samad, A hybrid approach of adaptive wavelet transform, long short-term memory and ARIMA-GARCH family models for the stock index prediction, Expert Syst. Appl. 182 (2021).
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Published: 18 February 2025
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