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

TL;DR

This paper introduces a hybrid time series forecasting method combining ARIMA and polynomial classifiers, achieving improved accuracy over individual models across various real-world datasets.

Contribution

The study presents a novel hybrid approach that leverages the strengths of ARIMA and polynomial classifiers for enhanced forecasting performance.

Findings

Hybrid model outperforms individual models in accuracy

Achieves better forecasting results across multiple datasets

Slight increase in computational time for improved accuracy

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.