A Hybrid BAT-SVR Methodology for Forecasting Iraq's Interest Rates of Commercial Bank (IRCB) Time Series Data
Hybrid BAT-SVR Methodology
DOI:
https://doi.org/10.63841/23555Keywords:
Bat Algorithm (BA), Hybrid BAT-SVR, Time Series Forecasting, Support Vector Regression (SVR)Abstract
Time series forecasting plays a crucial role in economics and finance, particularly in predicting interest rates that influence monetary policy and financial decision-making. While previous research has explored optimizing similar models, this study is the first to apply an optimized model to Iraq's IRCB dataset. Accurately predicting interest rates in Iraq’s volatile economy remains a challenge, as traditional forecasting methods struggle with the dataset’s non-linear patterns in financial data, leading to suboptimal predictions. Additionally, selecting optimal hyperparameters for SVR is time-consuming and often ineffective. To address these issues, this study used a Hybrid BAT-SVR approach, leveraging the Bat Algorithm’s global search capabilities to automate and enhance SVR’s hyperparameter tuning. The BA is utilized to optimize SVR’s hyperparameters, enhancing its predictive accuracy and robustness in handling non-linear relationships in time series data. So that, the primary goal is to develop a reliable and accurate forecasting model for IRCB data. The proposed methodology is applied to Iraq’s Commercial Bank Interest Rates (IRCB) dataset, covering the period from June 2005 to June 2024. Empirical results demonstrate that the hybrid model outperforms standalone SVR and traditional forecasting methods in terms of prediction accuracy and generalization ability. Performance metrics, including R², Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC), confirm the efficiency of the BAT-SVR model in capturing complex financial trends. The findings provide a valuable tool for policymakers and financial institutions to enhance decision-making and economic planning. Furthermore, this study contributes to the growing field of hybrid machine learning models in financial time series forecasting, offering insights for future research in optimization-based predictive modelling.
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