Abstract
Forecasting stock prices have recently emerged as an essential component of the economic realm. Stock price forecasting is regarded as a challenging endeavor due to the volatility and noise of stock market activity. In many stock price prediction scenarios, the Facebook Prophet, LightGBM and ARIMAX models have been demonstrated to be competitive versus other models. This research presents an architecture based on a time series model, such as Facebook Prophet, Light Gradient Boost Machine (GBM), and Autoregressive Integrated Moving Average with Explanatory Variable (ARIMAX) to accurately predict stock prices. Experiments with multiple potential outcomes are conducted to evaluate the suggested framework using the stock price data set. The model was trained on ADANI stock price data over the previous fourteen years using Facebook Prophet, LightGBM and ARIMAX and evaluated using the Root Mean Square Error metric (RMSE).
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References
Nti, I.K., Adekoya, A.F., Weyori, B.A.: A systematic review of fundamental and technical analysis of stock market predictions. Artif. Intell. Rev. 53(4), 3007–3057 (2019)
Lin, Y.L., Lai, C.J., Pai, P.F.: Using deep learning techniques in forecasting stock markets by hybrid data with multilingual sentiment analysis. Electronics 11(21), 3513 (2022)
Bhanja, S., Das, A.: A black swan event-based hybrid model for Indian Stock Markets’ trends prediction. Innov. Syst. Softw. Eng. bhanja2022black, 1–5 (2022). https://doi.org/10.1007/s11334-021-00428-0
Jing, N., Wu, Z., Wang, H.: A hybrid model integrating deep learning with investor sentiment analysis for stock price prediction. Expert Syst. Appl. 178, 115019 (2021)
Sarvesh, S., Sidharth, R.V., Vaishnav, V., Thangakumar, J., Sathyalakshmi, S.: A hybrid model for stock price prediction using machine learning techniques with CNN. In: 5th International Conference on Information Systems and Computer Networks (ISCON) (2021)
Kumar, D., Meghwani, S.S., Thakur, M.: Proximal support vector machine-based hybrid prediction models for trend forecasting in financial markets. J. Comput. Sci. 17, 1–13 (2016)
Zheng, X., Xiong, N.: Stock price prediction based on PCA-LSTM model. In: 5th International Conference on Mathematics and Statistics (2022)
Md. Ebtidaul Karim, Md. Foysal, Sunanda Das,: Stock Price Prediction Using Bi-LSTM and GRU-Based Hybrid Deep Learning Approach. In: Ashish Khanna, Deepak Gupta, Vineet Kansal, Giancarlo Fortino, Aboul Ella Hassanien, (ed.) Proceedings of Third Doctoral Symposium on Computational Intelligence: DoSCI 2022, pp. 701–711. Springer Nature Singapore, Singapore (2023). https://doi.org/10.1007/978-981-19-3148-2_60
Padhi, D., Padhy, D.N., Bhoi, A.K., Shafi, J., Yesuf, S.: An intelligent fusion model with portfolio selection and machine learning for stock market prediction. Comput. Intell. Neurosci. 06, 18 (2022)
Jiang, W.: Applications of deep learning in stock market prediction: re-cent progress. Expert Syst. Appl. 184, 115537 (2021)
Hao, Y., Gao, Q.: Predicting the trend of stock market index using the hybrid neural network based on multiple time scale feature learning. Appl. Sci. 10(11), 3961 (2020)
Nti, I., Adekoya, A., Weyori, B.: A comprehensive evaluation of ensemble learning for stock-market prediction. J. Big Data 7, 3 (2020)
Eapen, J., Bein, D., Verma, A.: Novel deep learning model with CNN and bi-directional LSTM for improved stock market index prediction. In: IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC) (2019)
Al-Thelaya, K.A., El-Alfy, E.S.M., Mohammed, S.: Forecasting of bahrain stock market with deep learning: methodology and case study. In: 8th International Conference on Modeling Simulation and Applied Optimization (ICMSAO), pp. 1–5. Manama, Bahrain (2019)
Borovkova, S., Tsiamas, I.: An ensemble of LSTM neural networks for high-frequency stock market classification. J. Forecast. 38(6), 600–619 (2019)
Long, W., Lu, Z., Cui, L.: Deep learning-based feature engineering for stock price movement prediction. Knowl.-Based Syst. 164, 163–173 (2019)
Kamara, A.F., Chen, E., Liu, Q., Pan, Z: Combining contextual neural networks for time series classification. Neurocomputing 384, 57–66 (2020).
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Singh, H., Malhotra, M. (2024). A Time Series Analysis-Based Stock Price Prediction Framework Using Artificial Intelligence. In: Challa, R.K., et al. Artificial Intelligence of Things. ICAIoT 2023. Communications in Computer and Information Science, vol 1930. Springer, Cham. https://doi.org/10.1007/978-3-031-48781-1_22
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DOI: https://doi.org/10.1007/978-3-031-48781-1_22
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