A Foundation Model for Instruction-Conditioned In-Context Time Series Tasks

• URL: http://arxiv.org/abs/2603.22586v1
• Date: Mon, 23 Mar 2026 21:24:41 GMT
• Title: A Foundation Model for Instruction-Conditioned In-Context Time Series Tasks
• Abstract summary: In-context learning (ICL) allows a model to adapt at inference time by conditioning on examples rather than updating parameters.<n>We present a foundation model for instruction-conditioned in-context time-series tasks based on a quantile-regression T5 encoder-decoder.
• Score: 0.7677431882779153
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In-context learning (ICL) allows a model to adapt at inference time by conditioning on examples rather than updating parameters. Existing time-series foundation models use implicit positional context, retrieval, or task-specific objectives, but rarely explicit instruction-conditioned demonstrations. We present a foundation model for instruction-conditioned in-context time-series tasks based on a quantile-regression T5 encoder-decoder. Historical examples and queries are encoded with a structured tokenization scheme that marks target series, covariates, context, and task-specific future information. A hierarchical Transformer with per-example encoding, example-level fusion, and cross-example attention conditions decoding on demonstration pairs, enabling forecasting and related tasks without task-specific fine-tuning. We train on large-scale real and synthetic time series using supervised forecasting plus self-supervised tasks, including imputation, reconstruction, classification, anomaly detection, and source demixing. This multi-task training learns a distribution over task mappings and improves adaptation to local structure at inference time. Across diverse datasets, frequencies, and horizons, our method outperforms strong foundation baselines on point and probabilistic forecasting benchmarks, including fev-bench and GIFT-Eval, while remaining competitive on classification and anomaly detection.

Related papers

• ChorusTIC: Training-Free Multivariate Time Series Classification via Chorus In-Context Learning [52.72143039958434]
  We introduce ChorusTIC, a classification-native foundation model for in-context classification across heterogeneous channel configurations.<n>ChorusTIC maps variable channel configurations into a fixed-width representation independent of the original channel count.<n>It calibrates feature axes using context-derived distributions and predicts query labels through leakage-protected in-context learning.
  arXiv  Detail & Related papers  (2026-08-25T03:45:24Z)
• In-Context Multiple Instance Learning [47.70525189635083]
  We show that pretraining an in-context learner with a Perceiver-style architecture on synthetic data yields a model that can solve new tasks from a handful of labeled bags.<n>A model pretrained on a mixture of these generators inherits their per-task strengths and achieves the best average performance across twelve MIL benchmarks.
  arXiv  Detail & Related papers  (2026-06-04T17:50:32Z)
• TS-ICL: A Flexible Time-Indexed Foundation Model for Time Series via In-Context Learning [0.764671395172401]
  Current approaches primarily focus on forecasting, while real-world time series are often irregularly and partially observed.<n>We introduce TS-ICL, a novel probabilistic In-Context Learning encoder--regressor Transformer that unifies forecasting and imputation.
  arXiv  Detail & Related papers  (2026-06-04T08:52:21Z)
• It's TIME: Towards the Next Generation of Time Series Forecasting Benchmarks [87.7937890373758]
  Time series foundation models (TSFMs) are revolutionizing the forecasting landscape from specific dataset modeling to generalizable task evaluation.<n>We introduce TIME, a next-generation task-centric benchmark comprising 50 fresh datasets and 98 forecasting tasks.<n>We propose a novel pattern-level evaluation perspective that moves beyond traditional dataset-level evaluations based on static meta labels.
  arXiv  Detail & Related papers  (2026-02-12T16:31:01Z)
• Estimating Time Series Foundation Model Transferability via In-Context Learning [74.65355820906355]
  Time series foundation models (TSFMs) offer strong zero-shot forecasting via large-scale pre-training.<n>Fine-tuning remains critical for boosting performance in domains with limited public data.<n>We introduce TimeTic, a transferability estimation framework that recasts model selection as an in-context-learning problem.
  arXiv  Detail & Related papers  (2025-09-28T07:07:13Z)
• Unlocking the Power of Mixture-of-Experts for Task-Aware Time Series Analytics [18.97715342585514]
  Time Series Analysis is widely used in various real-world applications such as weather forecasting, financial fraud detection, imputation for missing data in IoT systems, and classification for action recognization.<n>MoE, as a powerful architecture, still falls short in adapting to versatile tasks in time series analytics due to its task-agnostic router and the lack of capability in modeling channel correlations.<n>We propose a novel, general MoE-based time series framework called PatchMoE to support the intricate knowledge'' utilization for distinct tasks, thus task-aware.
  arXiv  Detail & Related papers  (2025-09-26T12:44:46Z)
• A Wireless Foundation Model for Multi-Task Prediction [50.21098141769079]
  We propose a unified foundation model for multi-task prediction in wireless networks that supports diverse prediction intervals.<n>After trained on large-scale datasets, the proposed foundation model demonstrates strong generalization to unseen scenarios and zero-shot performance on new tasks.
  arXiv  Detail & Related papers  (2025-07-08T12:37:55Z)
• Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction [20.1863553357121]
  Current deep learning architectures for remote sensing are fundamentally rigid.<n>We introduce the Spatial-Temporal-Spectral Unified Network (STSUN) for unified modeling.<n> STSUN can adapt to input and output data with arbitrary spatial sizes, temporal lengths, and spectral bands.<n>It unifies various dense prediction tasks and diverse semantic class predictions.
  arXiv  Detail & Related papers  (2025-05-18T07:39:17Z)
• Towards Generalisable Time Series Understanding Across Domains [10.350643783811174]
  We introduce a novel pre-training paradigm specifically designed to handle time series heterogeneity.<n>We propose a tokeniser with learnable domain signatures, a dual masking strategy, and a normalised cross-correlation loss.<n>Our code and pre-trained weights are available at https://www.oetu.com/oetu/otis.
  arXiv  Detail & Related papers  (2024-10-09T17:09:30Z)
• UniTS: A Unified Multi-Task Time Series Model [31.675845788410246]
  UniTS is a unified multi-task time series model that integrates predictive and generative tasks into a single framework. UniTS is tested on 38 datasets across human activity sensors, healthcare, engineering, and finance.
  arXiv  Detail & Related papers  (2024-02-29T21:25:58Z)
• Time-series Generation by Contrastive Imitation [87.51882102248395]
  We study a generative framework that seeks to combine the strengths of both: Motivated by a moment-matching objective to mitigate compounding error, we optimize a local (but forward-looking) transition policy. At inference, the learned policy serves as the generator for iterative sampling, and the learned energy serves as a trajectory-level measure for evaluating sample quality.
  arXiv  Detail & Related papers  (2023-11-02T16:45:25Z)
• Continuous-time convolutions model of event sequences [46.3471121117337]
  Event sequences are non-uniform and sparse, making traditional models unsuitable. We propose COTIC, a method based on an efficient convolution neural network designed to handle the non-uniform occurrence of events over time. COTIC outperforms existing models in predicting the next event time and type, achieving an average rank of 1.5 compared to 3.714 for the nearest competitor.
  arXiv  Detail & Related papers  (2023-02-13T10:34:51Z)
• Ti-MAE: Self-Supervised Masked Time Series Autoencoders [16.98069693152999]
  We propose a novel framework named Ti-MAE, in which the input time series are assumed to follow an integrate distribution. Ti-MAE randomly masks out embedded time series data and learns an autoencoder to reconstruct them at the point-level. Experiments on several public real-world datasets demonstrate that our framework of masked autoencoding could learn strong representations directly from the raw data.
  arXiv  Detail & Related papers  (2023-01-21T03:20:23Z)
• Interpretable Feature Construction for Time Series Extrinsic Regression [0.028675177318965035]
  In some application domains, it occurs that the target variable is numerical and the problem is known as time series extrinsic regression (TSER) We suggest an extension of a Bayesian method for robust and interpretable feature construction and selection in the context of TSER. Our approach exploits a relational way to tackle with TSER: (i), we build various and simple representations of the time series which are stored in a relational data scheme, then, (ii), a propositionalisation technique is applied to build interpretable features from secondary tables to "flatten" the data.
  arXiv  Detail & Related papers  (2021-03-15T08:12:19Z)

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.