发表机构
Fractal（Fractal）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究比较多种模型预测中子监测器时间序列，发现量子启发QiKAN误差最低，而简单季节性基线也极具竞争力，表明强先验可超越复杂架构。
AI 中文摘要
我们针对Lomnicky Stit中子监测器（LMKS）时间序列，提出了一项聚焦且可复现的多步预测研究。我们的评估套件涵盖简单季节性基线、现代深度序列模型以及函数型和量子启发架构，包括Seasonal Naive、长短期记忆网络（LSTM）、时间卷积网络（TCN）、N-BEATS、Kolmogorov-Arnold网络（KAN）以及两种量子启发变体QiLSTM和QiKAN。我们描述了数据集特征、诊断分析、预处理流程和训练过程，并报告了所有评估模型使用平均绝对误差（MAE）和均方根误差（RMSE）的聚合点预测性能。我们的快速运行结果表明，量子启发KAN变体QiKAN在评估配置中实现了最低的聚合预测误差，而简单的Seasonal Naive基线仍然极具竞争力。这些结果表明，对于高度周期性的科学监测时间序列，纳入强季节性或低维函数先验的模型可以匹配或显著超越更复杂的序列架构。这些发现促使我们进一步研究用于预测周期性科学信号的简约且可分解的函数逼近器。
英文摘要
We present a focused and reproducible study of multi-horizon forecasting on the Lomnicky Stit neutron monitor (LMKS) time series. Our evaluation suite covers simple seasonal baselines, modern deep sequence models, and functional and quantum-inspired architectures, including Seasonal Naive, Long Short-Term Memory (LSTM), Temporal Convolutional Network (TCN), N-BEATS, Kolmogorov-Arnold Networks (KAN), and two quantum-inspired variants, QiLSTM and QiKAN. We describe the dataset characteristics, diagnostic analysis, preprocessing pipeline, and training procedures, and report aggregate point-forecast performance using mean absolute error (MAE) and root mean squared error (RMSE) for all evaluated models. Our quick-run results indicate that the quantum-inspired KAN variant, QiKAN, achieves the lowest aggregate forecasting error among the evaluated configurations, while the simple Seasonal Naive baseline remains remarkably competitive. These results suggest that, for highly periodic scientific monitoring time series, models incorporating strong seasonal or low-dimensional functional priors can match or outperform substantially more complex sequence architectures. The findings motivate further investigation of parsimonious and decomposable function approximators for forecasting periodic scientific signals.
Comments14 pages, 3 figures, 1 table, accepted at the Computing Conference 2026
