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

• URL: http://arxiv.org/abs/2505.06874v2
• Date: Tue, 27 May 2025 03:16:32 GMT
• Title: Enhancing Time Series Forecasting via a Parallel Hybridization of ARIMA and Polynomial Classifiers
• Authors: Thanh Son Nguyen, Van Thanh Nguyen, Dang Minh Duc Nguyen,
• Abstract summary: We propose a hybrid forecasting approach that integrates the ARIMA model with a classifier to leverage the complementary strengths of both models.<n>The proposed hybrid model consistently outperforms the individual models in terms of prediction accuracy, al-beit with a modest increase in execution time.
• Score: 1.9799527196428246
• License: http://creativecommons.org/licenses/by/4.0/
• 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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