Pre-Model Structure Screening Across Non-Stationary Financial and Neural Time Series
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2026
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| _version_ | 1866901914698907648 |
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| author | Lee, Parker |
| author_facet | Lee, Parker |
| contents | <p>Time series from diverse domains such as financial markets and neuroscience are frequently analyzed using statistical and machine-learning models that implicitly assume the presence of stable underlying structure. In practice, however, such signals are often highly non-stationary, regime-dependent, and noisy, raising the question of whether and when downstream modeling assumptions are valid.</p> <p>This work presents a <strong>cross-domain evaluation of a pre-model structure screening framework</strong>, applied to two fundamentally different classes of data: financial market indices and electroencephalographic (EEG) recordings. Using a magnitude-based coherence index computed in a sliding-window manner, the method assesses time-varying structural organization in raw signals without relying on task labels, frequency bands, prediction targets, or domain-specific modeling assumptions.</p> <p>Across both financial and neural time series, the analysis reveals intermittent and smoothly varying regimes of structural coherence that are not apparent from visual inspection of the raw data alone. Despite their distinct origins and interpretations, both domains exhibit analogous patterns of transient structural stability and instability, suggesting that pre-model structure screening captures <strong>domain-agnostic properties of complex, non-stationary systems</strong>.</p> <p>The contribution of this work is methodological rather than inferential. The proposed framework is intended as an <strong>upstream diagnostic and gatekeeping step</strong>, informing when modeling, inference, or prediction may be structurally admissible, and when such efforts may be unreliable. By synthesizing results across finance and neuroscience, this paper positions pre-model structure screening as a general tool for responsible modeling in complex data environments.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18112213 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Pre-Model Structure Screening Across Non-Stationary Financial and Neural Time Series Lee, Parker pre-model diagnostics structure screening structure detection structural validity coherence analysis non-stationary time series time-varying structure regime dynamics complex systems cross-domain analysis domain-agnostic methods financial time series market dynamics EEG electroencephalography neural signals time series analysis sliding-window analysis <p>Time series from diverse domains such as financial markets and neuroscience are frequently analyzed using statistical and machine-learning models that implicitly assume the presence of stable underlying structure. In practice, however, such signals are often highly non-stationary, regime-dependent, and noisy, raising the question of whether and when downstream modeling assumptions are valid.</p> <p>This work presents a <strong>cross-domain evaluation of a pre-model structure screening framework</strong>, applied to two fundamentally different classes of data: financial market indices and electroencephalographic (EEG) recordings. Using a magnitude-based coherence index computed in a sliding-window manner, the method assesses time-varying structural organization in raw signals without relying on task labels, frequency bands, prediction targets, or domain-specific modeling assumptions.</p> <p>Across both financial and neural time series, the analysis reveals intermittent and smoothly varying regimes of structural coherence that are not apparent from visual inspection of the raw data alone. Despite their distinct origins and interpretations, both domains exhibit analogous patterns of transient structural stability and instability, suggesting that pre-model structure screening captures <strong>domain-agnostic properties of complex, non-stationary systems</strong>.</p> <p>The contribution of this work is methodological rather than inferential. The proposed framework is intended as an <strong>upstream diagnostic and gatekeeping step</strong>, informing when modeling, inference, or prediction may be structurally admissible, and when such efforts may be unreliable. By synthesizing results across finance and neuroscience, this paper positions pre-model structure screening as a general tool for responsible modeling in complex data environments.</p> |
| title | Pre-Model Structure Screening Across Non-Stationary Financial and Neural Time Series |
| topic | pre-model diagnostics structure screening structure detection structural validity coherence analysis non-stationary time series time-varying structure regime dynamics complex systems cross-domain analysis domain-agnostic methods financial time series market dynamics EEG electroencephalography neural signals time series analysis sliding-window analysis |
| url | https://doi.org/10.5281/zenodo.18112213 |