Adaptive Multi-Scale Forecasting and Gate-Localized Conformal Prediction for Multivariate Nonstationary Time Series

• URL: http://arxiv.org/abs/2607.23165v1
• Date: Sat, 25 Jul 2026 11:46:38 GMT
• Title: Adaptive Multi-Scale Forecasting and Gate-Localized Conformal Prediction for Multivariate Nonstationary Time Series
• Abstract summary: ABF-T-GLCP is a model-agnostic framework for forecasting and uncertainty quantification in nonstationary time series.<n>The forecasting module combines horizon-specific temporal experts through a learned gate and refines predictions using sparse predictive transfer.<n>The uncertainty module, Gate-Localized Conformal Prediction, uses the learned gate state, together with temporal recency, to select locally relevant calibration residuals.
• Score: 7.704601239153111
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose ABF-T-GLCP, a model-agnostic framework for forecasting and uncertainty quantification in nonstationary multivariate time series. The central idea is to learn an adaptive predictive state representation for point forecasting and reuse it for conformal calibration. The forecasting module combines horizon-specific temporal experts through a learned gate and refines predictions using sparse predictive transfer across related series. The uncertainty module, Gate-Localized Conformal Prediction (GLCP), uses the learned gate state, together with temporal recency, to select locally relevant calibration residuals, thereby coupling uncertainty calibration to the predictive regimes used by the forecasting model. This shared representation allows point forecasts and prediction intervals to adapt consistently under evolving temporal dynamics while retaining the model-agnostic nature of conformal prediction and yielding approximate local coverage under mild stability conditions. Experiments on a large-scale high-frequency commodity forecasting benchmark show consistent gains in point forecasting accuracy and substantially narrower prediction intervals with empirical coverage close to the nominal level. Additional results indicate that the framework extends beyond the motivating financial application.

Related papers

• SPACE: Sample-cloud Predictive Adaptive Conformal Ellipsoids for Multivariate Time-Series Forecasting [0.42056926734482064]
  SPACE is a conformal wrapper for sample-generating multivariate forecasters.<n>It constructs ellipsoidal joint prediction regions by estimating time-local covariance geometry directly from the current forecast sample cloud.<n>It consistently brings realized joint and rolling coverage closer to the nominal target.
  arXiv  Detail & Related papers  (2026-08-18T03:44:32Z)
• TSCoNet: A Two-Stage Copula CNN-LSTM for Uncertainty-Aware Spatio-Temporal Forecasting [7.191212864694664]
  TSCoNet is a two-stage convolutional-recurrent model coupled with a Gaussian copula that jointly forecasts multiple variables over space and time.<n>We study the approach on simulated non-stationary spatial fields on the sphere and on a real dataset of monthly precipitation and temperature for fifty cities over 2000-2020.
  arXiv  Detail & Related papers  (2026-07-11T17:28:53Z)
• Selective Time Series Forecasting via Metalearning [3.255705667028885]
  We propose a selective forecasting framework that addresses the limitation by modeling the empirical percentile of forecasting errors.<n>By decoupling the rejection decision from the forecast itself and grounding it in domain-agnostic features, the framework enables effective abstention transfer across heterogeneous time series.
  arXiv  Detail & Related papers  (2026-06-22T15:02:10Z)
• TimeAPN: Adaptive Amplitude-Phase Non-Stationarity Normalization for Time Series Forecasting [67.91113180885601]
  TimeAPN explicitly models and predicts non-stationary factors from both the time and frequency domains.<n>TimeAPN consistently improves long-term forecasting accuracy across multiple prediction horizons.<n>It outperforms state-of-the-art reversible normalization methods.
  arXiv  Detail & Related papers  (2026-03-18T07:21:24Z)
• Distribution-informed Online Conformal Prediction [53.674678995825666]
  We propose Conformal Optimistic Prediction (COP), an online conformal prediction algorithm incorporating underlying data pattern into the update rule.<n>COP produces tighter prediction sets when predictable pattern exists, while retaining valid coverage guarantees even when estimates are inaccurate.<n>We prove that COP can achieve valid coverage and construct shorter prediction intervals than other baselines.
  arXiv  Detail & Related papers  (2025-12-08T17:51:49Z)
• Predictive inference for time series: why is split conformal effective despite temporal dependence? [8.032656343027146]
  Conformal prediction methods provide distribution-free coverage for any iid or exchangeable data distribution.<n>Using predictors with "memory" -- i.e., predictors that utilize past observations, such as autoregressive models -- further exacerbates this problem.<n>Our results bound the loss of coverage of these methods in terms of a new "switch coefficient", measuring the extent to which temporal dependence within the time series creates violations of exchangeability.
  arXiv  Detail & Related papers  (2025-10-02T18:24:04Z)
• Adaptive Conformal Prediction Intervals Over Trajectory Ensembles [50.31074512684758]
  Future trajectories play an important role across domains such as autonomous driving, hurricane forecasting, and epidemic modeling.<n>We propose a unified framework based on conformal prediction that transforms sampled trajectories into calibrated prediction intervals with theoretical coverage guarantees.
  arXiv  Detail & Related papers  (2025-08-18T21:14:07Z)
• Feature Fitted Online Conformal Prediction for Deep Time Series Forecasting Model [0.8287206589886881]
  Time series forecasting is critical for many applications, where deep learning-based point prediction models have demonstrated strong performance.<n>Existing confidence interval modeling approaches suffer from key limitations.<n>We propose a lightweight predictoral prediction method that provides valid coverage and shorter interval lengths without retraining.
  arXiv  Detail & Related papers  (2025-05-13T01:33:53Z)
• Relational Conformal Prediction for Correlated Time Series [56.59852921638328]
  We address the problem of uncertainty quantification in time series by exploiting correlated sequences.<n>We propose a novel distribution-free approach based on conformal prediction framework and quantile regression.<n>Our approach provides accurate coverage and achieves state-of-the-art uncertainty quantification in relevant benchmarks.
  arXiv  Detail & Related papers  (2025-02-13T16:12:17Z)
• Self-Calibrating Conformal Prediction [16.606421967131524]
  We introduce Self-Calibrating Conformal Prediction to deliver calibrated point predictions alongside prediction intervals with finite-sample validity conditional on these predictions. We show that our method improves calibrated interval efficiency through model calibration and offers a practical alternative to feature-conditional validity.
  arXiv  Detail & Related papers  (2024-02-11T21:12:21Z)
• When Rigidity Hurts: Soft Consistency Regularization for Probabilistic Hierarchical Time Series Forecasting [69.30930115236228]
  Probabilistic hierarchical time-series forecasting is an important variant of time-series forecasting. Most methods focus on point predictions and do not provide well-calibrated probabilistic forecasts distributions. We propose PROFHiT, a fully probabilistic hierarchical forecasting model that jointly models forecast distribution of entire hierarchy.
  arXiv  Detail & Related papers  (2023-10-17T20:30:16Z)
• When Rigidity Hurts: Soft Consistency Regularization for Probabilistic Hierarchical Time Series Forecasting [69.30930115236228]
  Probabilistic hierarchical time-series forecasting is an important variant of time-series forecasting. Most methods focus on point predictions and do not provide well-calibrated probabilistic forecasts distributions. We propose PROFHiT, a fully probabilistic hierarchical forecasting model that jointly models forecast distribution of entire hierarchy.
  arXiv  Detail & Related papers  (2022-06-16T06:13:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.