Empirical Comparison of Lightweight Forecasting Models for Seasonal and Non-Seasonal Time Series

TL;DR

This study empirically compares Polynomial Classifier and RBF Neural Network models across diverse real-world time series, highlighting their respective strengths in accuracy, speed, and interpretability for seasonal and non-seasonal data.

Contribution

It provides a comprehensive empirical evaluation of two lightweight forecasting models on multiple datasets, offering practical guidance for model selection based on data characteristics.

Findings

Polynomial Classifier is more accurate and faster for non-seasonal series.

RBFNN outperforms on series with strong seasonal patterns.

Polynomial model is more interpretable and transparent.

Abstract

Accurate time series forecasting is essential in many real-time applications that demand both high predictive accuracy and computational efficiency. This study provides an empirical comparison between a Polynomial Classifier and a Radial Basis Function Neural Network (RBFNN) across four real-world time series datasets (weather conditions, gold prices, crude oil prices, and beer production volumes) that cover both seasonal and nonseasonal patterns. Model performance is evaluated by forecasting accuracy (using Mean Absolute Error, Root Mean Squared Error, and Coefficient of Variation of Root Mean Squared Error) and computational time to assess each model's viability for real time forecasting. The results show that the PC yields more accurate and faster forecasts for non seasonal series, whereas the RBFNN performs better on series with pronounced seasonal patterns. From an interpretability standpoint, the polynomial model offers a simpler, more transparent structure (in contrast to the black box nature of neural network), which is advantageous for understanding and trust in real time decision making. The performance differences between PC and RBFNN are statistically significant, as confirmed by paired t tests and Wilcoxon signed rank tests. These findings provide practical guidance for model selection in time series forecasting, indicating that PC may be preferable for quick, interpretable forecasts in non-seasonal contexts, whereas RBFNN is superior for capturing complex seasonal behaviors