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ARXIV:2603.17439 · TIME SERIES FORECASTING · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.17439TIME SERIES FORECASTINGSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
Baguan-TS is a novel Transformer model that enhances time series forecasting through in-context learning and raw-sequence representation.
Opportunity summary
Pain Baguan-TS is a novel Transformer model that enhances time series forecasting through in-context learning and raw-sequence representation.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
Baguan-TS is a novel Transformer model that enhances time series forecasting through in-context learning and raw-sequence representation. We bridge this gap with a unified framework, Baguan-TS, which integrates the raw-sequence representation learning with ICL,…
Transformers enable in-context learning (ICL) for rapid, gradient-free adaptation in time series forecasting, yet most ICL-style approaches rely on tabularized, hand-crafted features, while end-to-end sequence models lack inference-time adaptation. We bridge this gap with…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Transformers enable in-context learning (ICL) for rapid, gradient-free adaptation in time series forecasting, yet most ICL-style approaches rely on tabularized, hand-crafted features, while end-to-end…
Time Series Forecasting moved forward this cycle; last verified April 2026. Public score 7.0/10.
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Score7.0Public score shown from the verified overall while the stale axis breakdown refreshesAnalysis summary
Baguan-TS is a novel Transformer model that enhances time series forecasting through in-context learning and raw-sequence representation.
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Paper Pack
10.48550/arXiv.2603.17439Baguan-TS is a novel Transformer model that enhances time series forecasting through in-context learning and raw-sequence representation.
Abstract
Transformers enable in-context learning (ICL) for rapid, gradient-free adaptation in time series forecasting, yet most ICL-style approaches rely on tabularized, hand-crafted features, while end-to-end sequence models lack inference-time adaptation. We bridge this gap with a unified framework, Baguan-TS, which integrates the raw-sequence representation learning with ICL, instantiated by a 3D Transformer that attends jointly over temporal, variable, and context axes. To make this high-capacity model practical, we tackle two key hurdles: (i) calibration and training stability, improved with a feature-agnostic, target-space retrieval-based local calibration; and (ii) output oversmoothing, mitigated via context-overfitting strategy. On public benchmark with covariates, Baguan-TS consistently outperforms established baselines, achieving the highest win rate and significant reductions in both point and probabilistic forecasting metrics. Further evaluations across diverse real-world energy datasets demonstrate its robustness, yielding substantial improvements.
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Extraction status
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Proof status
unverified0 refs; 0 sources; 17% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
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Dimensions overall score 7.0
PROBLEM
Baguan-TS is a novel Transformer model that enhances time series forecasting through in-context learning and raw-sequence representation. We bridge this gap with a unified framework, Baguan-TS, which integrates the raw-sequence representation learning with ICL, instantiated by a...
METHOD
Transformers enable in-context learning (ICL) for rapid, gradient-free adaptation in time series forecasting, yet most ICL-style approaches rely on tabularized, hand-crafted features, while end-to-end sequence models lack inference-time adaptation. We bridge this gap with a unif...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Transformers enable in-context learning (ICL) for rapid, gradient-free adaptation in time series forecasting, yet most ICL-style approaches rely on tabularized, hand-crafted features, while end-to-end seq...
WHY NOW
Time Series Forecasting moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed public claims while anchored extraction refreshes.
Baguan-TS is a novel Transformer model that enhances time series forecasting through in-context learning and raw-sequence representation. We bridge this gap with a unified framework, Baguan-TS, which integrates the raw-sequence representation learning with ICL, instantiated by a 3D Transformer that attends jointly over temporal, variable, and context axes.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Transformers enable in-context learning (ICL) for rapid, gradient-free adaptation in time series forecasting, yet most ICL-style approaches rely on tabularized, hand-crafted features, while end-to-end sequence models lack inference-time adaptation. We bridge this gap with a unified framework, Baguan-TS, which integrates the raw-sequence representation learning with ICL, instantiated by a 3D Transformer that attends jointly over temporal, variable, and context axes.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Transformers enable in-context learning (ICL) for rapid, gradient-free adaptation in time series forecasting, yet most ICL-style approaches rely on tabularized, hand-crafted features, while end-to-end sequence models lack inference-time adaptation.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Time Series Forecasting moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Concepts
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Markets
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Baguan-TS is a novel Transformer model that enhances time series forecasting through in-context learning and raw-sequence representation.
Segment
Time Series Forecasting
Adoption evidence
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Commercial read
7.0/10 public viability
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reason
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proof status
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next verification path
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Evidence coverage
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passport absent
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Artifact maturity
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Technical feasibility
partial
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Gaps
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Run minimal reproduction from the Build Passport prototype path.
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Write integration checklist from prototype path and target workflow.
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ARTIFACTS
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DEFENSIBILITY
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