Declaration of Competing Interest

Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgement
This work is supported by The National Natural Science Foundation of China (no. 71904064) and The Natural Science Foundation of Jiangsu Province (no. BK) partially supports this research. The research is also supported by the 111 Project and the Fundamental Research Funds for the Central Universities (Grant No JUSRP11922).
References
Ballings, M, Van den Poel, D, Hespeels, N, & Gryp, R. (2015). Evaluating multiple classifiers for stock price direction prediction. Expert Systems with Applications, 42
(20), 7046–7056. https://doi.org/10.1016/j.eswa.2015.05.013
Bartiromo, R. (2004). Nonlinear behaviour of stock market volatility. Retrieved October
01, 2004, from https://ui.adsabs.harvard.edu/abs/2004cond.mat.10768B cond-ma t/0410768
Basak, S, Kar, S, Saha, S, Khaidem, L, & Dey, SR. Predicting the direction of stock market prices using tree-based classifiers. The North American Journal of
Economics and Finance, 47, 552–567. https://doi.org/10.1016/j.najef.2018.06.013
Bollerslev, T, Marrone, J, Xu, L, & Zhou, H. (2014). Stock Return Predictability and Variance Risk Premia: Statistical Inference and International Evidence. The Journal
of Financial and Quantitative Analysis, 49(3), 633–661. http://www.jstor.org/stable/ Bruce, L. M, Mathur, A, & Byrd, J. JD. Denoising and Wavelet-Based Feature Extraction of MODIS Multi-Temporal Vegetation Signatures. GIScience & Remote
Sensing, 43(1), 67–77. https://doi.org/10.2747/1548-1603.43.1.67
Bustos, O, & Pomares-Quimbaya, A. (2020). Stock market movement forecast A Systematic review. Expert Systems with Applications, 156, Article 113464. https://doi. org/10.1016/j.eswa.2020.113464
Chandrashekar, G, & Sahin, FA survey on feature selection methods. Computers
&
Electrical Engineering, 40(1), 16–28. https://doi.org/10.1016/j.compeleceng.201 3.11.024
Dinesh, SN. RR, Anusha, SP SR April 2021). Prediction of Trends in Stock Market using Moving Averages and Machine Learning. 2021 6th International Conference for Convergence in Technology (I2CT).
Fama, E. F. (1965). Random Walks in Stock Market Prices. Financial Analysts Journal, 21
(5), 55–59. http://www.jstor.org/stable/4469865
Farias Naz´ario, RT, e Silva, J. L, Sobreiro, VA Kimura, HA literature review of technical analysis on stock markets. The Quarterly Review of Economics and
Finance, 66, 115-126. https://doi.org/https://doi.org/10.1016/j.qref.2017.01.014.
Fama, E. F. (1970). Efficient Capital Markets A Review of Theory and Empirical Work.
The Journal of Finance, 25(2), 383–417. https://doi.org/10.2307/2325486
Ferreira, MA Santa-Clara, P. (2011). Forecasting stock market returns The sum of the parts is more than the whole. Journal of Financial Economics, 100(3), 514–537. https://doi.org/10.1016/j.jfineco.2011.02.003
Guan, D, Yuan, W, Lee, Y.-K., Najeebullah, K, & Rasel, MK. A Review of Ensemble Learning Based Feature Selection. IETE Technical Review, 31(3), 190–198. https://doi.org/10.1080/02564602.2014.906859
Haq, AU, Zeb, A, Lei, Z, & Zhang, D. (2021). Forecasting daily stock trend using multi- filter feature selection and deep learning. Expert Systems with Applications, 168, Article 114444. https://doi.org/10.1016/j.eswa.2020.114444
Jiang, J, Ma, K, & Cai, X. (2007). Nonlinear characteristics and long-range correlations in Asian stock markets. Physica A Statistical Mechanics and its Applications, 378(2),
399–407. https://doi.org/10.1016/j.physa.2006.12.011
Jiang, W. (2021). Applications of deep learning in stock market prediction Recent progress. Expert Systems with Applications, 184, Article 115537. https://doi.org/10
.1016/j.eswa.2021.115537
Kompella, KC. D, Mannam, VG. R, & Rayapudi, SR. DWT based bearing fault detection in induction motor using noise cancellation. Journal of Electrical Systems
and Information Technology, 3(3), 411–427. https://doi.org/10.1016/j.jesit.2016.0 7.002
Kou, G, Xu, Y, Peng, Y, Shen, F, Chen, Y, Chang, K, & Kou, S. (2021). Bankruptcy prediction for SMEs using transactional data and two-stage multiobjective feature selection. Decision Support Systems, 140, Article 113429. https://doi.org/10.1016/j. dss.2020.113429
Li, Z, & Tam, V. (2017, 27 Nov Dec. 2017). Combining the real-time wavelet denoising and long-short-term-memory neural network for predicting stock indexes.
2017 IEEE Symposium Series on Computational Intelligence (SSCI). Long, J, Chen, Z, He, W, Wu, T, & Ren, J. (2020). An integrated framework of deep learning and knowledge graph for prediction of stock price trend An application in Chinese stock exchange market. Applied Soft Computing, 91, Article 106205. htt ps://doi.org/10.1016/j.asoc.2020.106205
Lu, C.-J. (2010). Integrating independent component analysis-based denoising scheme with neural network for stock price prediction. Expert Systems with Applications, 37
(10), 7056–7064. https://doi.org/10.1016/j.eswa.2010.03.012
Martínez, B, & Gilabert, MA. Vegetation dynamics from NDVI time series analysis using the wavelet transform. Remote Sensing of Environment, 113(9),
1823–1842. https://doi.org/10.1016/j.rse.2009.04.016
Masini, RP, Medeiros, MC Mendes, E. F. (2020). Machine Learning Advances for Time Series Forecasting. arXiv:2012.12802. Retrieved December 01, 2020, from https://ui.adsabs.harvard.edu/abs/2020arXiv201212802M.
Masset, P. (2015). Analysis of financial time series using wavelet methods. In. New York
Springer-Verlag
G. Ji et al.

Expert Systems With Applications 200 (2022) 116941
12
Mittal, A, & Goel, A. (2012). Stock prediction using twitter sentiment analysis. Standford
University, CS (2011 http://cs229. stanford. edu/proj2011/GoelMittal-
StockMarketPredictionUsingTwitterSentimentAnalysis. pdf), 15.
Naik, N, & Mohan, BR. Optimal Feature Selection of Technical Indicator and Stock Prediction Using Machine Learning Technique. Emerging Technologies in Computer Engineering Microservices in Big Data Analytics, Singapore. Patel, J, Shah, S, Thakkar, P, & Kotecha, K. (a. Predicting stock and stock price index movement using Trend Deterministic Data Preparation and machine learning techniques. Expert Systems with Applications, 42(1), 259–268. https://doi.org/10.10 16/j.eswa.2014.07.040
Patel, J, Shah, S, Thakkar, P, & Kotecha, Kb. Predicting stock market index using fusion of machine learning techniques. Expert Systems with Applications, 42(4),
2162–2172. https://doi.org/10.1016/j.eswa.2014.10.031
Phan, DH. B, Sharma, SS Narayan, PK. Stock return forecasting Some new evidence. International Review of Financial Analysis, 40, 38–51. https://doi. org/10.1016/j.irfa.2015.05.002
Rhif, M, Ben Abbes, A, Farah, IR, Martínez, B, & Sang, Ya. Wavelet Transform Application for/in Non-Stationary Time-Series Analysis A Review. Applied Sciences,
9(7), 1345. https://www.mdpi.com/2076-3417/9/7/1345
Rhif, M, Ben Abbes, A, Farah, IR, Martínez, B, & Sang, Yb. Wavelet Transform Application for/in Non-Stationary Time-Series Analysis A Review. Applied Sciences,
9(7). https://doi.org/10.3390/app9071345 Shah, D, Isah, H, & Zulkernine, F. (2019). Stock Market Analysis A Review and Taxonomy of Prediction Techniques. International Journal of Financial Studies, 7(2),
26. https://www.mdpi.com/2227-7072/7/2/26
Song, D, Baek, AM. C, & Kim, N. (2021). Forecasting Stock Market Indices Using
Padding-Based Fourier Transform Denoising and Time Series Deep Learning Models.
IEEE Access, 9, 83786–83796. https://doi.org/10.1109/ACCESS.2021.3086537
Verma, AK, Pal, S, & Kumar, S. (2020). Prediction of Skin Disease Using Ensemble Data Mining Techniques and Feature Selection Method—a Comparative Study. Applied
Biochemistry and Biotechnology, 190(2), 341–359. https://doi.org/10.1007/s12010- z
Vivekanandan, T, & Sriman Narayana Iyengar, NC. Optimal feature selection using a modified differential evolution algorithm and its effectiveness for prediction of heart disease. Computers in Biology and Medicine, 90, 125-136. https://doi.org/ https://doi.org/10.1016/j.compbiomed.2017.09.011. Wu, D, Wang, X, & Wu, SA Hybrid Method Based on Extreme Learning Machine and Wavelet Transform Denoising for Stock Prediction. Entropy, 23(4), 440. https://www.mdpi.com/1099-4300/23/4/440
Yan, H, & Ouyang, H. (2018). Financial Time Series Prediction Based on Deep Learning.
Wireless Personal Communications, 102(2), 683–700. https://doi.org/10.1007/ s
Zhang, J, Cui, S, Xu, Y, Li, Q, & Li, TA novel data-driven stock price trend prediction system. Expert Systems with Applications, 97, 60–69. https://doi.org/10.10 16/j.eswa.2017.12.026
G. Ji et al.