发表机构
Drakai Capital（Drakai Capital）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
StaFIR通过凸学习优化因果滤波器，平衡平稳性与输入相似性，在金融时间序列中实现自适应滤波，提升信号保真度且不牺牲预测精度。
AI 中文摘要
减少持续时间序列中的非平稳性需要决定去除其时间依赖性的程度。在金融领域，分数阶差分通常通过增广迪基-富勒（ADF）检验进行调优，这限制了搜索范围到单参数族的滞后分布，并且仅间接处理输入保留问题。我们提出StaFIR，一种因果有限脉冲响应滤波器，具有学习到的非负指数滞后分布的混合。其凸学习目标在经验平稳性与对输入的相似性之间取得平衡。我们在ARFIMA-GARCH受控设置和滚动金融序列上评估StaFIR，包括已实现波动率预测任务。实验表明，StaFIR根据持续性调整其滤波强度，同时在平稳状态下限制不必要的变换。在下游预测中，与固定半阶差分相比没有明显的精度差异，而StaFIR实现了对原始信号更高的测量相似性。一个补充的直接预测实验发现，更大的输入相似性与更小的预测惩罚相关，尽管原始表示仍然更强。
英文摘要
Reducing nonstationarity in a persistent time series entails deciding how much of its temporal dependence to remove. In finance, fractional differencing is often tuned using the Augmented Dickey--Fuller (ADF) test, limiting the search to a one-parameter family of lag profiles and addressing input preservation only indirectly. We propose StaFIR, a causal finite-impulse-response filter with a learned nonnegative mixture of exponential lag profiles. Its convex learning objective balances empirical stationarity with similarity to the input. We evaluate StaFIR on ARFIMA--GARCH controlled settings and rolling financial series, including a realized-volatility forecasting task. The experiments show that StaFIR adjusts its filtering strength to persistence while limiting unnecessary transformation in stationary regimes. In downstream forecasting, there is no clear accuracy difference from fixed half-order differencing, while StaFIR achieves higher measured similarity to the raw signal. A complementary direct forecasting experiment finds that greater input similarity is associated with smaller forecasting penalties, although the raw representation remains stronger.
CommentsAccepted at the TS-LIMITS Workshop at NeurIPS 2026
