A NOVEL DEEP-LEARNING BASED APPROACH FOR TIME SERIES FORECASTING USING SARIMA, NEURAL PROPHET AND FB PROPHET

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.