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New StaFIR filter learns optimal time series stationarity while preserving input similarity

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced StaFIR, a novel causal finite-impulse-response filter designed for time series analysis. Unlike traditional methods that rely on the Augmented Dickey--Fuller (ADF) test and limit parameter choices, StaFIR learns a mixture of exponential lag profiles. Its objective function balances achieving empirical stationarity with maintaining similarity to the original input signal. Experiments on financial data and simulated ARFIMA--GARCH settings indicate that StaFIR adapts its filtering strength to the series' persistence, avoiding excessive transformation when the data is already stationary. AI

RANK_REASON The cluster contains a research paper detailing a new method for time series analysis. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New StaFIR filter learns optimal time series stationarity while preserving input similarity

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The cluster contains a research paper detailing a new method for time series analysis. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Lorena Egger, Mathis Linger ·

    StaFIR: Convex Learning of Stationarity-Aware Causal Filters

    arXiv:2610.07430v1 Announce Type: new Abstract: 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 sear…

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