A novel deep-learning based approach for time series forecasting using sarima, neural prophet and fb prophet
Palabras clave:
Time-series analysis, Time-Series forecasting, artificial intelligence, SARIMA, Neural prophet model, Fb prophet model, Evaluation parametersResumen
Objective: The article aims to explore and evaluate a novel deep-learning approach for time series forecasting using three specific models: SARIMA (Seasonal Auto-Regressive Integrated Moving Average), Neural Prophet, and Facebook Prophet. The primary goal is to assess the effectiveness of these models in predicting stock market values in the Gulf region, providing insights into the best-suited models for forecasting tasks.
Methods: The study employs Python libraries and frameworks to implement the SARIMA, Neural Prophet, and Facebook Prophet models. The models are trained using stock market data from the Mulkia Gulf Real Estate dataset. The methodology includes data preprocessing, model training, evaluation, and comparison using metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE).
Results: The evaluation results show that SARIMA performs well in general prediction tasks, especially when datasets contain seasonality trends. Facebook Prophet excels with smaller datasets containing seasonal data, while Neural Prophet demonstrates its ability to capture complex, non-linear patterns. However, Neural Prophet requires more intricate data and fine-tuning for optimal results.
Contribution: This study provides a comparative analysis of deep-learning models for time series forecasting, offering valuable insights into their strengths and weaknesses. The findings contribute to the understanding of which models are most suitable for stock market prediction and how they can be adapted to different data types and scenarios.
Conclusion: The research concludes that each model—SARIMA, Facebook Prophet, and Neural Prophet—has its unique strengths in time series forecasting. SARIMA is reliable for handling seasonal data, Facebook Prophet is efficient for smaller datasets with clear trends, and Neural Prophet is best for more complex datasets. The study highlights the importance of selecting the appropriate model based on the specific requirements of the forecasting task.
Citas
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.
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Derechos de autor 2024 Journal of Management & Technology
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