Lightweight Wrappers for Adapting Time Series Foundation Models to Regional Drought Forecasting

• URL: http://arxiv.org/abs/2607.17511v1
• Date: Mon, 20 Jul 2026 03:36:15 GMT
• Title: Lightweight Wrappers for Adapting Time Series Foundation Models to Regional Drought Forecasting
• Abstract summary: Large emphTime Series Foundation Models (TSFMs) demonstrate strong zero-shot forecasting capabilities across diverse domains.<n>We introduce a lightweight, black-box adaptation framework that enhances frozen TSFMs at inference time.<n>We evaluate our framework on one month ahead Standardized Precipitation Evapotranspiration Index (SPEI) prediction across multiple sites in South Australia.
• Score: 26.173260602322383
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large \emph{Time Series Foundation Models} (TSFMs) demonstrate strong zero-shot forecasting capabilities across diverse domains. However, their application to regional climate forecasting faces practical challenges: model weights are often proprietary, local training records are limited, and computational budgets are constrained, making traditional fine-tuning approaches infeasible. To address these constraints, we introduce a lightweight, black-box adaptation framework (requiring no access to backbone parameters and no backbone fine-tuning) that enhances frozen TSFMs at inference time through two plug-and-play wrappers: \textbf{SMR\textsuperscript{2}} (Stationarity aware multi-resolution Residual), which decomposes the input into multi-resolution temporal views, learns stride specific residual corrections that capture regional dynamics, then adaptively ensembles them into a single forecast, and \textbf{MBB} (Moving Block Bootstrap), which preserves temporal dependencies through block resampling and ensembles over temporally coherent residual perturbations to stabilize the point forecast. Both wrappers instantiate the same bagging style principle: they build diverse views of the input or its residuals, forecast each with the same frozen backbone, and aggregate, so all adaptation comes from inference time ensembling rather than any weight update. Evaluated on one month ahead Standardized Precipitation Evapotranspiration Index (SPEI) prediction across multiple sites in South Australia, our framework consistently improves forecasting performance across several backbone models, demonstrating up to 26\% mean squared error (MSE) reduction over the corresponding frozen backbone while enabling practical deployment in resource constrained regional forecasting systems.

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)
• Making Every Step Count: Spatio-Temporal Information Allocation for Imaging Inverse Problems [63.904793414062816]
  We introduce two complementary and training-free components, i.e., Spectrum-based Scheduling (SAS) and Measurement-Prioritized Attention (MPA)<n>SAS distributes the available NFEs over flow time according to the spectrum and logSNR geometry, thus better balancing semantic exploration and detail refinement.<n>MPA guides information toward weakly constrained regions, thereby enhancing semantic and structural fidelity.
  arXiv  Detail & Related papers  (2026-08-12T07:39:22Z)
• Factorize to Generalize: Retrieval-Guided Invariant-Dynamic Decomposition for Time Series Forecasting [64.2640656619583]
  Time series foundation models (TSFMs) have recently achieved strong zero-shot forecasting performance through large-scale pretraining and retrieval-augmented prediction.<n>We propose a Retrieval-guided Invariant-Dynamic DEcomposition framework for time series forecasting.
  arXiv  Detail & Related papers  (2026-05-24T07:31:55Z)
• LeapTS: Rethinking Time Series Forecasting as Adaptive Multi-Horizon Scheduling [74.94985663101906]
  We present LeapTS, a novel framework that reformulates time series forecasting as a dynamic scheduling process over the prediction horizon.<n>We show that LeapTS improves overall forecasting performance by at least 7.4% while achieving a 2.6$times$ to 5.3$times$ inference speedup over representative Transformer-based models.
  arXiv  Detail & Related papers  (2026-05-11T09:54:02Z)
• PnP-Corrector: A Universal Correction Framework for Coupled Spatiotemporal Forecasting [62.22445401483844]
  Existing methods are severely constrained by the bottleneck of compounding errors.<n>We propose a universal framework called -Cast-Corrector (Plug-and-Play Corrector)<n>Our method significantly enhances the long-term stability of coupled forecasting systems.
  arXiv  Detail & Related papers  (2026-05-09T13:12:33Z)
• 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)
• A Decomposition-based State Space Model for Multivariate Time-Series Forecasting [0.0]
  We propose an end-to-end decomposition framework using three parallel deep state space model branches to capture trend, seasonal, and residual components.<n>Across standard benchmarks, DecompSSM outperformed strong baselines, indicating the effectiveness of combining component-wise deep state space models and global context refinement.
  arXiv  Detail & Related papers  (2026-02-05T07:17:08Z)
• ForecastGAN: A Decomposition-Based Adversarial Framework for Multi-Horizon Time Series Forecasting [0.5213778368155993]
  Time series forecasting is essential across domains from finance to supply chain management.<n>This paper introduces ForecastGAN, a novel decomposition based adversarial framework for multi-horizon predictions.<n>ForecastGAN consistently outperforms state-of-the-art transformer models for short-term forecasting while remaining competitive for long-term horizons.
  arXiv  Detail & Related papers  (2025-11-06T15:19:23Z)
• Bridging the Last Mile of Prediction: Enhancing Time Series Forecasting with Conditional Guided Flow Matching [9.465542901469815]
  Conditional Guided Flow Matching (CGFM) is a model-agnostic framework that extends flow matching by integrating outputs from an auxiliary predictive model.<n>CGFM incorporates historical data as both conditions and guidance, uses two-sided conditional paths, and employs affine paths to expand the path space.<n> Experiments across datasets and baselines show CGFM consistently outperforms state-of-the-art models, advancing forecasting.
  arXiv  Detail & Related papers  (2025-07-09T18:03:31Z)
• Elucidated Rolling Diffusion Models for Probabilistic Weather Forecasting [52.6508222408558]
  We introduce Elucidated Rolling Diffusion Models (ERDM)<n>ERDM is the first framework to unify a rolling forecast structure with the principled, performant design of Elucidated Diffusion Models (EDM)<n>On 2D Navier-Stokes simulations and ERA5 global weather forecasting at 1.5circ resolution, ERDM consistently outperforms key diffusion-based baselines.
  arXiv  Detail & Related papers  (2025-06-24T21:44:31Z)
• OneForecast: A Universal Framework for Global and Regional Weather Forecasting [67.61381313555091]
  We propose a global-regional nested weather forecasting framework (OneForecast) based on graph neural networks.<n>By combining a dynamic system perspective with multi-grid theory, we construct a multi-scale graph structure and densify the target region.<n>We introduce an adaptive messaging mechanism, using dynamic gating units, to deeply integrate node and edge features for more accurate extreme event forecasting.
  arXiv  Detail & Related papers  (2025-02-01T06:49:16Z)
• Meta-Forecasting by combining Global DeepRepresentations with Local Adaptation [12.747008878068314]
  We introduce a novel forecasting method called Meta Global-Local Auto-Regression (Meta-GLAR) It adapts to each time series by learning in closed-form the mapping from the representations produced by a recurrent neural network (RNN) to one-step-ahead forecasts. Our method is competitive with the state-of-the-art in out-of-sample forecasting accuracy reported in earlier work.
  arXiv  Detail & Related papers  (2021-11-05T11:45:02Z)

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.