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arXiv · 2505.06874

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

Thanh Son Nguyen·Van Thanh Nguyen·Dang Minh Duc Nguyen
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Abstract

Time series forecasting has attracted significant attention, leading to the de-velopment of a wide range of approaches, from traditional statistical meth-ods to advanced deep learning models. Among them, the Auto-Regressive Integrated Moving Average (ARIMA) model remains a widely adopted linear technique due to its effectiveness in modeling temporal dependencies in economic, industrial, and social data. On the other hand, polynomial classifi-ers offer a robust framework for capturing non-linear relationships and have demonstrated competitive performance in domains such as stock price pre-diction. In this study, we propose a hybrid forecasting approach that inte-grates the ARIMA model with a polynomial classifier to leverage the com-plementary strengths of both models. The hybrid method is evaluated on multiple real-world time series datasets spanning diverse domains. Perfor-mance is assessed based on forecasting accuracy and computational effi-ciency. Experimental results reveal that the proposed hybrid model consist-ently outperforms the individual models in terms of prediction accuracy, al-beit with a modest increase in execution time.

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Thanh Son Nguyen, Van Thanh Nguyen, Dang Minh Duc Nguyen. 2025-05-27. Enhancing Time Series Forecasting via a Parallel Hybridization of ARIMA and Polynomial Classifiers. https://arxiv.org/abs/2505.06874

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