<ul><li>Time series forecasting is crucial and has various approaches from traditional statistical methods to advanced deep learning models.</li><li>ARIMA model is effective in modeling temporal dependencies while polynomial classifiers capture non-linear relationships well.</li><li>A hybrid forecasting approach combining ARIMA and polynomial classifiers has been proposed for enhanced forecasting accuracy.</li><li>Experimental results show that the hybrid model outperforms individual models in terms of prediction accuracy with a slight increase in execution time.</li></ul>

Enhancing Time Series Forecasting via a Parallel Hybridization of ARIMA and Polynomial Classifiers

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