Stock market predictions using FastRNN-based model. (English) Zbl 1496.62176
Giri, Debasis (ed.) et al., Proceedings of the seventh international conference on mathematics and computing, ICMC 2021, Shibpur, India, March 2–5, 2021. Singapore: Springer. Adv. Intell. Syst. Comput. 1412, 439-450 (2022).
Summary: Predicting the correct values of stocks in fast fluctuating high-frequency financial data is always a challenging task. Existing state-of-the-art models are very efficient in terms of accuracy but lags in prediction speed. In this work, we aim to develop a deep-learning-based fast model for live predictions of stock values with minimum errors. The proposed model is based on fast recurrent neural networks (FastRNNs), which provides us with both of the desired features. We have considered the 1-min time interval stock data of four companies for a period of one day. The model is aimed to have a low computational complexity as well so that it can be run for live predictions as well. The model’s performance is measured by root mean square error (RMSE) along with computation time. The model outperforms LSTM, CNN, and other deep learning models for live predictions of stock values.
For the entire collection see [Zbl 1491.65006].
For the entire collection see [Zbl 1491.65006].
MSC:
| 62P05 | Applications of statistics to actuarial sciences and financial mathematics |
| 62M10 | Time series, auto-correlation, regression, etc. in statistics (GARCH) |
| 62M20 | Inference from stochastic processes and prediction |
| 68T07 | Artificial neural networks and deep learning |
Keywords:
fast recurrent neural network (FastRNN); convolutional neural network (CNN); time series forecasting; root mean square error (RMSE); stock price predictionReferences:
| [1] | Abraham A, Nath B, Mahanti PK (2001) Hybrid intelligent systems for stock market analysis. In: International conference on computational science. Springer, pp 337-345 · Zbl 0983.68648 |
| [2] | Ahmed MS, Cook AR (1979) Analysis of freeway traffic time-series data by using Box-Jenkins techniques. No 722 in Urban systems operations, Transportation Research Board |
| [3] | Akita R, Yoshihara A, Matsubara T, Uehara K (2016) Deep learning for stock prediction using numerical and textual information. In: 2016 IEEE/ACIS 15th international conference on computer and information science (ICIS). IEEE, pp 1-6 |
| [4] | Ariyo AA, Adewumi AO, Ayo CK (2014) Stock price prediction using the arima model. In: 2014 UKSim-AMSS 16th international conference on computer modelling and simulation. IEEE, pp 106-112 |
| [5] | Arjovsky M, Shah A, Bengio Y (2016) Unitary evolution recurrent neural networks. In: International conference on machine learning, pp 1120-1128 |
| [6] | Armano, G.; Marchesi, M.; Murru, A., A hybrid genetic-neural architecture for stock indexes forecasting, Inf Sci, 170, 1, 3-33 (2005) · doi:10.1016/j.ins.2003.03.023 |
| [7] | Chen K, Zhou Y, Dai F (2015) A lstm-based method for stock returns prediction: a case study of china stock market. In: 2015 IEEE international conference on big data (big data). IEEE, pp 2823-2824 |
| [8] | Chen, W.; Yeo, CK; Lau, CT; Lee, BS, Leveraging social media news to predict stock index movement using RNN-boost, Data Knowl Eng, 118, 14-24 (2018) · doi:10.1016/j.datak.2018.08.003 |
| [9] | Chikkakrishna NK, Hardik C, Deepika K, Sparsha N (2019) Short-term traffic prediction using sarima and fbprophet. In: 2019 IEEE 16th India council international conference (INDICON). IEEE, pp 1-4 |
| [10] | Choudhry, R.; Garg, K., A hybrid machine learning system for stock market forecasting, World Acad Sci Eng Technol, 39, 3, 315-318 (2008) |
| [11] | Ding X, Zhang Y, Liu T, Duan J (2015) Deep learning for event-driven stock prediction. In: Twenty-fourth international joint conference on artificial intelligence |
| [12] | Egeli, B., Stock market prediction using artificial neural networks, Dec Sup Syst, 22, 171-185 (2003) |
| [13] | Enke, D.; Thawornwong, S., The use of data mining and neural networks for forecasting stock market returns, Expert Syst Appl, 29, 4, 927-940 (2005) · doi:10.1016/j.eswa.2005.06.024 |
| [14] | Fischer, T.; Krauss, C., Deep learning with long short-term memory networks for financial market predictions, Eur J Oper Res, 270, 2, 654-669 (2018) · Zbl 1403.91387 · doi:10.1016/j.ejor.2017.11.054 |
| [15] | Franses, PH; Van Dijk, D., Forecasting stock market volatility using (non-linear) garch models, J Forecast, 15, 3, 229-235 (1996) · doi:10.1002/(SICI)1099-131X(199604)15:3<229::AID-FOR620>3.0.CO;2-3 |
| [16] | Fu J, Lum KS, Nguyen MN, Shi J (2007) Stock prediction using fcmac-byy. In: International symposium on neural networks. Springer, pp 346-351 |
| [17] | Gers FA, Eck D, Schmidhuber J (2002) Applying lstm to time series predictable through time-window approaches. In: Neural Nets WIRN Vietri-01. Springer, pp 193-200 · Zbl 1005.68945 |
| [18] | Gudelek MU, Boluk SA, Ozbayoglu AM (2017) A deep learning based stock trading model with 2-D CNN trend detection. In: 2017 IEEE symposium series on computational intelligence (SSCI). IEEE, pp 1-8 |
| [19] | He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770-778 |
| [20] | Huang, SJ; Shih, KR, Short-term load forecasting via ARMA model identification including non-gaussian process considerations, IEEE Trans Power Syst, 18, 2, 673-679 (2003) · doi:10.1109/TPWRS.2003.811010 |
| [21] | Jing, L.; Gulcehre, C.; Peurifoy, J.; Shen, Y.; Tegmark, M.; Soljacic, M.; Bengio, Y., Gated orthogonal recurrent units: on learning to forget, Neural Comput, 31, 4, 765-783 (2019) · Zbl 1476.68232 · doi:10.1162/neco_a_01174 |
| [22] | Kim T, Kim HY (2019) Forecasting stock prices with a feature fusion lstm-cnn model using different representations of the same data. PloS one 14(2):e0212320 |
| [23] | Kusupati A, Singh M, Bhatia K, Kumar A, Jain P, Varma M (2018) Fastgrnn: a fast, accurate, stable and tiny kilobyte sized gated recurrent neural network. In: Advances in neural information processing systems, pp 9017-9028 |
| [24] | Le, T.; Vo, MT; Vo, B.; Hwang, E.; Rho, S.; Baik, SW, Improving electric energy consumption prediction using CNN and BI-LSTM, Appl Sci, 9, 20, 4237 (2019) · doi:10.3390/app9204237 |
| [25] | Mohan S, Mullapudi S, Sammeta S, Vijayvergia P, Anastasiu DC (2019) Stock price prediction using news sentiment analysis. In: 2019 IEEE fifth international conference on big data computing service and applications (BigDataService). IEEE, pp 205-208 |
| [26] | Paliwal, N.; Vanjani, P.; Liu, JW; Saini, S.; Sharma, A., Image processing-based intelligent robotic system for assistance of agricultural crops, Int J Soc Hum Comput, 3, 2, 191-204 (2019) |
| [27] | Pawar K, Jalem RS, Tiwari V (2019) Stock market price prediction using LSTM RNN. In: Emerging trends in expert applications and security. Springer, pp 493-503 |
| [28] | Qiu J, Wang B, Zhou C (2020) Forecasting stock prices with long-short term memory neural network based on attention mechanism. PloS one 15(1):e0227222 |
| [29] | Saini S, Sahula V (2020) Chapter 11—setting up a neural machine translation system for English to Indian languages. In: Sinha G, Suri JS (eds) Cognitive informatics, computer modelling, and cognitive science. Academic Press, pp 195-212. doi:10.1016/B978-0-12-819443-0.00011-8, http://www.sciencedirect.com/science/article/pii/B9780128194430000118 |
| [30] | Saini S, Sahula V (2020) Cognitive architecture for natural language comprehension. Cogn Comput Syst 2(1):23-31 doi:10.1049/ccs.2019.0017, doi:10.1049/ccs.2019.0017 |
| [31] | Tay FE, Cao L (2001) Application of support vector machines in financial time series forecasting. Omega 29(4):309-317 |
| [32] | Taylor, SJ; Letham, B., Forecasting at scale, Am Stat, 72, 1, 37-45 (2018) · Zbl 07663916 · doi:10.1080/00031305.2017.1380080 |
| [33] | Tsai C, Wang S ()209 Stock price forecasting by hybrid machine learning techniques. In: Proceedings of the international multiconference of engineers and computer scientists, vol 1, p 60 |
| [34] | Wang M, Cheng J, Zhai H (2020) Life prediction for machinery components based on CNN-bilstm network and attention model. In: 2020 IEEE 5th information technology and mechatronics engineering conference (ITOEC). IEEE, pp 851-855 |
| [35] | Yadav K, Lamba A, Gupta D, Gupta A, Karmakar P, Saini S (2020) Bilingual sentiment analysis for a code-mixed Punjabi English social media text. In: 2020 5th international conference on computing, communication and security (ICCCS) (2020), pp 1-5. doi:10.1109/ICCCS49678.2020.9277309 |
| [36] | Zhang J, Lei Q, Dhillon IS (2018) Stabilizing gradients for deep neural networks via efficient SVD parameterization (2018). arXiv:1803.09327 |
| [37] | Zhang, Y.; Zheng, J.; Jiang, Y.; Huang, G.; Chen, R., A text sentiment classification modeling method based on coordinated CNN-LSTM-attention model, Chinese J Electron, 28, 1, 120-126 (2019) · doi:10.1049/cje.2018.11.004 |
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.
