对比两种轻量模型在有无季节性数据上的表现，指导实际预测场景选型。
Empirical Comparison of Lightweight Forecasting Models for Seasonal and Non-Seasonal Time Series
- 用多项式分类器和RBF神经网络对比不同时间序列的预测效果。
- 非季节性数据上多项式模型更准更快，季节性数据上RBFNN表现更好。
- 多项式模型结构简单易懂，适合需要可解释性的实时决策场景。
准确的时间序列预测在众多实时应用中至关重要，需兼顾高精度与计算效率。本研究基于四个真实世界时间序列数据集（天气、黄金价格、原油价格、啤酒产量）对多项式分类器（PC）与径向基函数神经网络（RBFNN）进行实证比较，涵盖季节性与非季节性模式。通过均值绝对误差（MAE）、均方根误差（RMSE）及变异系数（CVRMSE）评估预测精度，并以计算时间为指标衡量实时可行性。结果表明：在非季节性序列上，PC具有更高精度与更快速度；而在明显季节性序列上，RBFNN表现更优。从可解释性角度看，多项式模型结构更透明，优于神经网络的黑箱特性，有利于实时决策中的可信度建立。配对t检验与威尔科克森符号秩检验确认了两者性能差异具有统计显著性。研究为时间序列建模提供实用选型依据：非季节性场景推荐使用快速可解释的PC，季节性复杂模式则更适合使用RBFNN。
原文摘要 · Abstract (English)
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
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