NOVEL DEEP-LEARNING BASED APPROACH FOR TIME SERIES FORECASTING USING SARIMA, NEURAL PROPHET AND FB PROPHET
DOI:
https://doi.org/10.20397/2177-6652/2024.v24i2.2818Palavras-chave:
Time-series analysis, Time-Series forecasting, artificial intelligence, SARIMA, Neural prophet model, Fb prophet model, Evaluation parametersResumo
Today, there is a time series analysis that has gained significant popularity across various industries to provide effective solutions for any specific challenges. Artificial intelligence logic is generally used to solve time-series problems with the help of various frameworks that are available for the development of AI-based applications. Currently, there is a significant framework in Python that is driven by its comprehensive libraries and their practical implementations. Time series analysis can be built with the help of some specific models that give results to differentiate the efficiency and domains of great work. This paper focuses on three diverse libraries of Python to predict the Gulf stock exchange market. These libraries are prediction models that are helpful in predicting future values. These models utilized include SARIMA (Seasonal Auto-Regressive Integrated Moving Average), Neural Prophet Model, and Fb Prophet model. Each model is trained using the provided datasets, which are followed by evaluation parameters such as MSE, RMSE, recall, MDAPE, accuracy, precision, etc. The main purpose of using these three models for stock market prediction is to find the best-suited models. In the past few decades, the stock market industry has seemed to have promising growth after 2022 onwards. This paper will help us make a significant decision about model usage for addressing such complex challenges. Especially, the research findings indicate that all three models demonstrate in different domains. SARIMA can perform well in overall prediction tasks, while the Prophet model works great with smaller datasets that contain seasonality trends.
Referências
Bergmeir, C., Hyndman, R. J., & Koo, B. (2018). A note on the validity of cross-validation for evaluating autoregressive time series prediction. Computational Statistics & Data Analysis, 120, 70-83.
Cerqueira, V., Torgo, L., & Mozetič, I. (2020). Evaluating time series forecasting models: An empirical study on performance estimation methods. Machine Learning, 109, 1997-2028.
Chatfield, C (2000). Time-series forecasting. CRC press.
González Mata, A. (2020). A comparison between LSTM and Facebook Prophet models: a financial forecasting case study.
Jha, B. K., & Pande, S. (2021). Time series forecasting model for supermarket sales using FB-prophet. In 2021 5th International Conference on Computing Methodologies and Communication (ICCMC) (pp. 547-554). IEEE.
Khare, K., Darekar, O., Gupta, P., & Attar, V. Z. (2017, May). Short term stock price prediction using deep learning. In 2017 2nd IEEE international conference on recent trends in electronics, information & communication technology (RTEICT) (pp. 482-486). IEEE.
Kritharas, P. (2014). Developing a SARIMAX model for monthly wind speed forecasting in the uk (Doctoral dissertation, Loughborough University).
Long, B., Tan, F., & Newman, M. (2023). Forecasting the Monkeypox Outbreak Using ARIMA, Prophet, NeuralProphet, and LSTM Models in the United States. Forecasting, 5(1), 127-137.
Madsen, H. (2007). Time series analysis. CRC Press.
Mahmud, S. (2020). Bangladesh COVID-19 daily cases time series analysis using Facebook prophet model. Available at SSRN 3660368.
Masini, R. P., Medeiros, M. C., & Mendes, E. F. (2023). Machine learning advances for time series forecasting. Journal of economic surveys, 37(1), 76-111.Nikou, M., Mansourfar, G., & Bagherzadeh, J. (2019). Stock price prediction using DEEP learning algorithm and its comparison with machine learning algorithms. Intelligent Systems in Accounting, Finance and Management, 26(4), 164-174.
Nontapa, C., Kesamoon, C., Kaewhawong, N., & Intrapaiboon, P. (2021). A New Hybrid Forecasting Using Decomposition Method with SARIMAX Model and Artificial Neural Network. Computer Science, 16(4), 1341-1354.
Samek, W., Montavon, G., Lapuschkin, S., Anders, C. J., & Müller, K. R. (2021). Explaining deep neural networks and beyond: A review of methods and applications. Proceedings of the IEEE, 109(3), 247-278.
Shohan, M. J. A., Faruque, M. O., & Foo, S. Y. (2022). Forecasting of electric load using a hybrid LSTM-neural prophet model. Energies, 15(6), 2158.
Sivaramakrishnan, S., Fernandez, T. F., Babukarthik, R. G., & Premalatha, S. (2022). Forecasting time series data using arima and facebook prophet models. In Big data management in Sensing (pp. 47-59). River Publishers.
Torres, J. F., Hadjout, D., Sebaa, A., Martínez-Álvarez, F., & Troncoso, A. (2021). Deep learning for time series forecasting: a survey. Big Data, 9(1), 3-21.
Triebe, O., Hewamalage, H., Pilyugina, P., Laptev, N., Bergmeir, C., & Rajagopal, R. (2021). Neuralprophet: Explainable forecasting at scale. arXiv preprint arXiv:2111.15397.
Zunic, E., Korjenic, K., Hodzic, K., & Donko, D. (2020). Application of facebook's prophet algorithm for successful sales forecasting based on real-world data. arXiv preprint arXiv:2005.07575.
Downloads
Publicado
Como Citar
Edição
Seção
Licença
Copyright (c) 2024 Revista Gestão & Tecnologia
Este trabalho está licenciado sob uma licença Creative Commons Attribution-NonCommercial 4.0 International License.
Os direitos, inclusive os de tradução, são reservados. É permitido citar parte de artigos sem autorização prévia desde que seja identificada a fonte. A reprodução total de artigos é proibida. Em caso de dúvidas, consulte o Editor.
