ARXIV:2602.22066 · TIME SERIES FORECASTING · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

DualWeaver: Synergistic Feature Weaving Surrogates for Multivariate Forecasting with Univariate Time Series Foundation Models

arXiv

DualWeaver transforms univariate time series models into powerful multivariate forecasters with its innovative surrogate feature weaving framework.

Blocked on Code›Score7.0Evidence unverified

Opportunity summary

Pain DualWeaver transforms univariate time series models into powerful multivariate forecasters with its innovative surrogate feature weaving framework.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

DualWeaver transforms univariate time series models into powerful multivariate forecasters with its innovative surrogate feature weaving framework. To address this, we propose DualWeaver, a novel framework that adapts univariate TSFMs (Uni-TSFMs) for multivariate forecasting…

METHOD

Full abstract

Time-series foundation models (TSFMs) have achieved strong univariate forecasting through large-scale pre-training, yet effectively extending this success to multivariate forecasting remains challenging. To address this, we propose DualWeaver, a novel framework that adapts univariate TSFMs (Uni-TSFMs) for multivariate forecasting by using a pair of learnable, structurally symmetric surrogate series. Generated by a shared auxiliary feature-fusion module that captures cross-variable dependencies, these surrogates are mapped to TSFM-compatible series via the forecasting objective. The symmetric structure enables parameter-free reconstruction of final predictions directly from the surrogates, without additional parametric decoding. A theoretically grounded regularization term is further introduced to enhance robustness against adaptation collapse. Extensive experiments on diverse real-world datasets show that DualWeaver outperforms state-of-the-art multivariate forecasters in both accuracy and stability. We release the code at https://github.com/li-jinpeng/DualWeaver.

RESULT

ScienceToStartup currently rates this 7.0/10 on the public viability pass. The symmetric structure enables parameter-free reconstruction of final predictions directly from the surrogates, without additional parametric decoding.

WHY NOW

Time Series Forecasting moved forward this cycle; last verified April 2026. Public score 7.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score7.0

PainDualWeaver transforms univariate time series models into powerful multivariate forecasters with its innovative surrogate feature weaving framework.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

DualWeaver transforms univariate time series models into powerful multivariate forecasters with its innovative surrogate feature weaving framework.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

ARXIV:2602.22066 · TIME SERIES FORECASTING · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

DualWeaver: Synergistic Feature Weaving Surrogates for Multivariate Forecasting with Univariate Time Series Foundation Models

arXiv

DualWeaver transforms univariate time series models into powerful multivariate forecasters with its innovative surrogate feature weaving framework.

Blocked on Code›Score7.0Evidence unverified

Opportunity summary

Pain DualWeaver transforms univariate time series models into powerful multivariate forecasters with its innovative surrogate feature weaving framework.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

METHOD

Full abstract

RESULT

WHY NOW

Time Series Forecasting moved forward this cycle; last verified April 2026. Public score 7.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score7.0

PainDualWeaver transforms univariate time series models into powerful multivariate forecasters with its innovative surrogate feature weaving framework.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

DualWeaver transforms univariate time series models into powerful multivariate forecasters with its innovative surrogate feature weaving framework.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Paper Pack

10.48550/arXiv.2602.22066

DualWeaver: Synergistic Feature Weaving Surrogates for Multivariate Forecasting with Univariate Time Series Foundation Models

DualWeaver transforms univariate time series models into powerful multivariate forecasters with its innovative surrogate feature weaving framework.

Abstract

Source availability

PDF linked

The paper record includes a public PDF URL.

Extraction status

Derived fallback

Read summaries are estimated from adjacent metadata, not verified extraction rows.

Proof status

unverified

0 refs; 0 sources; 17% coverage.

What was readable

linkedon filenot materializedderived fallback29 indexednot indexed

Derived fallback: Estimated from adjacent evidence; not verified from source.

Viability

7.0

Time to MVP

MVP estimate missing

Commercial

No commercial flags on file

Export

Preparing verified analysis

lens / founder

PROBLEM

METHOD

RESULT

WHY NOW

Time Series Forecasting moved forward this cycle; last verified April 2026. Public score 7.0/10.

Claim map

Abstract-backed public claims while anchored extraction refreshes.

Strong 0Mixed 0Weak 4

Evidencepartial
DualWeaver transforms univariate time series models into powerful multivariate forecasters with its innovative surrogate feature weaving framework. To address this, we propose DualWeaver, a novel framework that adapts univariate TSFMs (Uni-TSFMs) for multivariate forecasting by using a pair of learnable, structurally symmetric surrogate series.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Time-series foundation models (TSFMs) have achieved strong univariate forecasting through large-scale pre-training, yet effectively extending this success to multivariate forecasting remains challenging. To address this, we propose DualWeaver, a novel framework that adapts univariate TSFMs (Uni-TSFMs) for multivariate forecasting by using a pair of learnable, structurally symmetric surrogate series.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. The symmetric structure enables parameter-free reconstruction of final predictions directly from the surrogates, without additional parametric decoding.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Time Series Forecasting moved forward this cycle; last verified April 2026. Public score 7.0/10.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial

Constellation map

Paper-native neighborhood for concepts, methods, materials, markets, and competitors. Missing lanes stay labeled instead of disappearing behind commercialization gates.

Open full Signal Canvas

Concepts

not indexed

Methods

Materials

PDF linked

Markets

Time Series Forecasting

Competitors

not indexed

Competitive landscape

DualWeaver transforms univariate time series models into powerful multivariate forecasters with its innovative surrogate feature weaving framework.

Segment

Time Series Forecasting

Adoption evidence

No public code link in the paper record yet

Commercial read

7.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Buzz

No indexed public discussion is attached to 2602.22066 yet. That is a visibility signal, not a blank module: the monitor is watching the public channels below.

Hacker News

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Bluesky

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PDF

Preview the source document here, or use the hero PDF action for a new tab.

References(29)

Time Series Foundation Models for Multivariate Financial Time Series Forecasting

2025Ben A. Marconi

TimePro: Efficient Multivariate Long-term Time Series Forecasting with Variable- and Time-Aware Hyper-state

2025Xiaowen Ma, Zhenliang Ni et al.

AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting

2025Abdelhakim Benechehab, Vasilii Feofanov et al.

Sundial: A Family of Highly Capable Time Series Foundation Models

2025Yong Liu, Guo Qin et al.

Generalized Prompt Tuning: Adapting Frozen Univariate Time Series Foundation Models for Multivariate Healthcare Time Series

2024Mingzhu Liu, Angela H. Chen et al.

Timer-XL: Long-Context Transformers for Unified Time Series Forecasting

2024Yong Liu, Guo Qin et al.

Deep Time Series Models: A Comprehensive Survey and Benchmark

2024Yuxuan Wang, Haixu Wu et al.

UniTST: Effectively Modeling Inter-Series and Intra-Series Dependencies for Multivariate Time Series Forecasting

2024Juncheng Liu, Chenghao Liu et al.

TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting

2024Shiyu Wang, Haixu Wu et al.

Foundation Models for Time Series Analysis: A Tutorial and Survey

2024Yuxuan Liang, Haomin Wen et al.

Chronos: Learning the Language of Time Series

2024Abdul Fatir Ansari, Lorenzo Stella et al.

TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variables

2024Yuxuan Wang, Haixu Wu et al.

Only the Curve Shape Matters: Training Foundation Models for Zero-Shot Multivariate Time Series Forecasting through Next Curve Shape Prediction

2024Cheng Feng, Long Huang et al.

MOMENT: A Family of Open Time-series Foundation Models

2024Mononito Goswami, Konrad Szafer et al.

Unified Training of Universal Time Series Forecasting Transformers

2024Gerald Woo, Chenghao Liu et al.

Tiny Time Mixers (TTMs): Fast Pre-trained Models for Enhanced Zero/Few-Shot Forecasting of Multivariate Time Series

2024Vijay Ekambaram, Arindam Jati et al.

iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

2023Yong Liu, Tengge Hu et al.

Exploring Progress in Multivariate Time Series Forecasting: Comprehensive Benchmarking and Heterogeneity Analysis

2023Zezhi Shao, Fei Wang et al.

Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictors

2023Yong Liu, Chenyu Li et al.

A Time Series is Worth 64 Words: Long-term Forecasting with Transformers

2022Yuqi Nie, Nam H. Nguyen et al.

Showing 20 of 29 references

CITED BY

No citing papers are indexed in the public S2S graph yet. This is an explicit zero-signal state, not a hidden lookup.

Foundation

Prior WorkTS-Memory: Plug-and-Play Memory for Time Series Foundation Models

7.0

Extension

Builds On ThisFactorize to Generalize: Retrieval-Guided Invariant-Dynamic Decomposition for Time Series Forecasting

0.0

Commercially relevant

Higher ViabilityBack to the Future: Look-ahead Augmentation and Parallel Self-Refinement for Time Series Forecasting

8.0

Conflicting

Competing ApproachFalcon-X: A Time Series Foundation Model for Heterogeneous Multivariate Modeling

7.0

Competing ApproachSDMixer: Sparse Dual-Mixer for Time Series Forecasting

5.0

Competing ApproachCoRA: Boosting Time Series Foundation Models for Multivariate Forecasting through Correlation-aware Adapter

7.0

Competing ApproachEnhancing Multivariate Time Series Forecasting with Global Temporal Retrieval

7.0

Competing ApproachOnline Irregular Multivariate Time Series Forecasting via Uncertainty-Driven Dual-Expert Calibration

7.0

Competing ApproachZero-shot Multivariate Time Series Forecasting Using Tabular Prior Fitted Networks

4.0

Competing ApproachAltTS: A Dual-Path Framework with Alternating Optimization for Multivariate Time Series Forecasting

7.0

Related Resources

What are the challenges in time series forecasting?(question)
How does TIFO improve time series forecasting?(question)
Why is time series forecasting important?(question)

Owned Distribution

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Agent drawer

5 surfaces preserved for agents. Humans can ignore.

Developer contracts, payload previews, evidence maps, and run controls stay here instead of the Read, Build, and Track workspace.

Run context

Paper: 2602.22066
Route: /paper/dualweaver-synergistic-feature-weaving-surrogates-for-multivariate-forecasting-with-univariate-time-series-foundation-mo
Active tab: read
Artifact: dualweaver-synergistic-feature-weaving-surrogates-for-multivariate-forecasting-with-univariate-time-series-foundation-mo

Available agents

Read extractor
Build planner
Track monitor
Competitive mapper
Related-paper scout

API/MCP endpoints

REST paper pack API/api/v1/paper/dualweaver-synergistic-feature-weaving-surrogates-for-multivariate-forecasting-with-univariate-time-series-foundation-mo/paper-pack
REST build passport API/api/v1/paper/dualweaver-synergistic-feature-weaving-surrogates-for-multivariate-forecasting-with-univariate-time-series-foundation-mo/build-passport
REST OpenAPI/api/openapi.json
MCP descriptor/api/mcp
MCP resourcesciencetostartup://surfaces/paper-workspace

Tool contracts

paper_packbuild_passportopportunity_kernelforesightsource_proofevidence_state

Payload preview

Inspect payload

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Schema validation

paper-r2 contract: present
JSON-LD twin: SSR emitted
OpenAPI path parity: /api/openapi.json
MCP resource parity: paper-workspace

Job trace

queued: drawer opened by user action
running: inspect or copy payload
succeeded: payload available in SSR
failed: route errors appear in evidence cards

Evidence map

sources used: page freshness, source proof anchors, JSON-LD
missing sources: exposed by PaperPack and EvidenceState chips
derived fallbacks: marked unverified before handoff

Page Freshness

Canonical route, proof status, last verified, refs, sources, and coverage.

Page Freshness

Paper proof surface

Canonical route: /paper/dualweaver-synergistic-feature-weaving-surrogates-for-multivariate-forecasting-with-univariate-time-series-foundation-mo

stale

Proof freshness: stale
Proof status: unverified
Display score: 7/10
Last proof check: 2026-04-02
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 17%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

OpenAlex: pending — this preprint is not yet indexed by OpenAlex.

Agent Handoff

Endpoint list, payload shape, route context, and copyable handoff data.

Agent Handoff

DualWeaver: Synergistic Feature Weaving Surrogates for Multivariate Forecasting with Univariate Time Series Foundation Models

Canonical ID dualweaver-synergistic-feature-weaving-surrogates-for-multivariate-forecasting-with-univariate-time-series-foundation-mo | Route /paper/dualweaver-synergistic-feature-weaving-surrogates-for-multivariate-forecasting-with-univariate-time-series-foundation-mo

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/dualweaver-synergistic-feature-weaving-surrogates-for-multivariate-forecasting-with-univariate-time-series-foundation-mo

MCP example

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source_context

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Buildability Receipt

Verdict, compute envelope, blockers, signature state, and receipt links.

Paper proof page receipt window

Watch and verify: DualWeaver: Synergistic Feature Weaving Surrogates for Multivariate Forecasting with Univariate Time Series Foundation Models

/buildability/dualweaver-synergistic-feature-weaving-surrogates-for-multivariate-forecasting-with-univariate-time-series-foundation-mo

Watchwatch

Subject: DualWeaver: Synergistic Feature Weaving Surrogates for Multivariate Forecasting with Univariate Time Series Foundation Models

Verdict

Watch

Verdict is Watch because viability or proof quality is intermediate and should be re-evaluated before execution.

Time to first demo

Insufficient data

No first-demo timestamp, owner estimate, or elapsed demo receipt is attached to this surface.

Compute envelope

Structured compute envelope

Insufficient data

No data, compute, hardware, memory, latency, dependency, or serving requirement receipt is attached.

Evidence ids

Receipt path

/buildability/dualweaver-synergistic-feature-weaving-surrogates-for-multivariate-forecasting-with-univariate-time-series-foundation-mo

Paper ref

dualweaver-synergistic-feature-weaving-surrogates-for-multivariate-forecasting-with-univariate-time-series-foundation-mo

arXiv id

2602.22066

Freshness

Generated at

2026-04-02T02:30:40.136Z

Evidence freshness

stale

Last verification

2026-04-02T02:30:40.136Z

Sources

References

Coverage

17%

Hash state

Lineage hash

b29facab1a6e9863ce7eb1ede77ca24f6928545d3a87d2bc34116f1549811478

Canonical opportunity-kernel lineage hash.

Signature state

External signature

unsigned_external

No founder, registry, pilot, or production-adoption signature is attached to this receipt.

Verification

not_verified

Verification is blocked until an external signature is provided.

Blockers

Missing: repo_url
Missing: references
Missing: proof_status
Missing: distribution_readiness_scores
Missing: paper_extraction_scorecards
Unknown: distribution readiness has not been computed yet
Unknown: proof verification has not been recorded yet

Verification pending / evidence receipt incomplete

repo_url

references

Missing proof, requirement, signature, approval, adoption, or telemetry fields are blockers and must not be inferred.

Open receipt API receipt Build Loop Signal Canvas Proof divergence Divergence API Brier outcomes API

Source Proof anchors

Visual citations from the paper document graph.

JSON-LD twin

The application/ld+json payload rendered for agents.

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DualWeaver: Synergistic Feature Weaving Surrogates for Multivariate Forecasting with Univariate Time Series Foundation Models

DualWeaver: Synergistic Feature Weaving Surrogates for Multivariate Forecasting with Univariate Time Series Foundation Models

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