Version 1
: Received: 13 November 2023 / Approved: 13 November 2023 / Online: 13 November 2023 (09:03:43 CET)
How to cite:
Albeladi, K.; Zafar, B.; Mueen, A. A Novel Deep-learning based Approach for Time Series Forecasting using SARIMA, Neural Prophet and Fb Prophet. Preprints2023, 2023110794. https://doi.org/10.20944/preprints202311.0794.v1
Albeladi, K.; Zafar, B.; Mueen, A. A Novel Deep-learning based Approach for Time Series Forecasting using SARIMA, Neural Prophet and Fb Prophet. Preprints 2023, 2023110794. https://doi.org/10.20944/preprints202311.0794.v1
Albeladi, K.; Zafar, B.; Mueen, A. A Novel Deep-learning based Approach for Time Series Forecasting using SARIMA, Neural Prophet and Fb Prophet. Preprints2023, 2023110794. https://doi.org/10.20944/preprints202311.0794.v1
APA Style
Albeladi, K., Zafar, B., & Mueen, A. (2023). A Novel Deep-learning based Approach for Time Series Forecasting using SARIMA, Neural Prophet and Fb Prophet. Preprints. https://doi.org/10.20944/preprints202311.0794.v1
Chicago/Turabian Style
Albeladi, K., Bassam Zafar and Ahmed Mueen. 2023 "A Novel Deep-learning based Approach for Time Series Forecasting using SARIMA, Neural Prophet and Fb Prophet" Preprints. https://doi.org/10.20944/preprints202311.0794.v1
Abstract
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 better-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.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
