Time Series Analysis: Hybrid Econometric - Machine Learning Model for Improved Financial Forecasting

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ID: 312606
2025
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Abstract
Financial forecasting in stock markets is a complex problem due to inherent volatility and non-linearity. This research proposes a hybrid Auto-Regressive Integrated Moving Average (ARIMA) - Convolutional Neural Network (CNN) - Support Vector Machine (SVM) model to enhance the accuracy of time-series predictions. The hybrid model integrates ARIMA for capturing linear trends, CNN for extracting non-linear features, and SVM for the final classification of trend directions. The study is conducted on an 8-year stock market dataset (2015-2023) from Euronext, with 21 features and 1787 observations. The Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and F1-score are used to evaluate performance. Results indicate that the hybrid model achieves a prediction accuracy of 59%, outperforming standalone ARIMA (48%) and CNN (54%). Comparative analysis with ARIMA-LSTM and ARIMA-RNN further validates the robustness of the proposed approach. This study contributes a novel econometric-machine learning hybrid framework for financial forecasting with superior predictive power.
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imported_1777055264_69ebb62068934 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Muhammad Arshad
Journal Journal of Computing & Biomedical Informatics
Year 2025
DOI
DOI not found
URL
https://jcbi.org/index.php/Main/article/view/1028
Keywords Keywords not found

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