ARXIV:2602.10847 · TIME SERIES FORECASTING · SUBMITTED 19 MAR · 18:48 UTC · FRESHNESS STALE

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

Enhancing Multivariate Time Series Forecasting with Global Temporal Retrieval

arXiv

The Global Temporal Retriever (GTR) enhances any forecasting model's accuracy by capturing long-term periodic patterns with minimal computational costs.

Blocked on Code›Score7.0Evidence unverified

Opportunity summary

Pain The Global Temporal Retriever (GTR) enhances any forecasting model's accuracy by capturing long-term periodic patterns with minimal computational costs.

Evidence 0 refs | 0 sources | 33% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

The Global Temporal Retriever (GTR) enhances any forecasting model's accuracy by capturing long-term periodic patterns with minimal computational costs. This limitation prevents them from effectively capturing global periodic patterns that often span cycles significantly…

METHOD

Full abstract

Multivariate time series forecasting (MTSF) plays a vital role in numerous real-world applications, yet existing models remain constrained by their reliance on a limited historical context. This limitation prevents them from effectively capturing global periodic patterns that often span cycles significantly longer than the input horizon - despite such patterns carrying strong predictive signals. Naive solutions, such as extending the historical window, lead to severe drawbacks, including overfitting, prohibitive computational costs, and redundant information processing. To address these challenges, we introduce the Global Temporal Retriever (GTR), a lightweight and plug-and-play module designed to extend any forecasting model's temporal awareness beyond the immediate historical context. GTR maintains an adaptive global temporal embedding of the entire cycle and dynamically retrieves and aligns relevant global segments with the input sequence. By jointly modeling local and global dependencies through a 2D convolution and residual fusion, GTR effectively bridges short-term observations with long-term periodicity without altering the host model architecture. Extensive experiments on six real-world datasets demonstrate that GTR consistently delivers state-of-the-art performance across both short-term and long-term forecasting scenarios, while incurring minimal parameter and computational overhead. These results highlight GTR as an efficient and general solution for enhancing global periodicity modeling in MTSF tasks. Code is available at this repository: https://github.com/macovaseas/GTR.

RESULT

ScienceToStartup currently rates this 7.0/10 on the public viability pass. Extensive experiments on six real-world datasets demonstrate that GTR consistently delivers state-of-the-art performance across both short-term and long-term forecasting scenarios, while incurring minimal parameter…

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

PainThe Global Temporal Retriever (GTR) enhances any forecasting model's accuracy by capturing long-term periodic patterns with minimal computational costs.

Evidence0 refs | 0 sources | 33% coverage

Blockermissing authors

Analysis summary

The Global Temporal Retriever (GTR) enhances any forecasting model's accuracy by capturing long-term periodic patterns with minimal computational costs.

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

ARXIV:2602.10847 · TIME SERIES FORECASTING · SUBMITTED 19 MAR · 18:48 UTC · FRESHNESS STALE

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

Enhancing Multivariate Time Series Forecasting with Global Temporal Retrieval

arXiv

The Global Temporal Retriever (GTR) enhances any forecasting model's accuracy by capturing long-term periodic patterns with minimal computational costs.

Blocked on Code›Score7.0Evidence unverified

Opportunity summary

Pain The Global Temporal Retriever (GTR) enhances any forecasting model's accuracy by capturing long-term periodic patterns with minimal computational costs.

Evidence 0 refs | 0 sources | 33% 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

PainThe Global Temporal Retriever (GTR) enhances any forecasting model's accuracy by capturing long-term periodic patterns with minimal computational costs.

Evidence0 refs | 0 sources | 33% coverage

Blockermissing authors

Analysis summary

The Global Temporal Retriever (GTR) enhances any forecasting model's accuracy by capturing long-term periodic patterns with minimal computational costs.

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

Paper Pack

10.48550/arXiv.2602.10847

Enhancing Multivariate Time Series Forecasting with Global Temporal Retrieval

The Global Temporal Retriever (GTR) enhances any forecasting model's accuracy by capturing long-term periodic patterns with minimal computational costs.

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; 33% coverage.

What was readable

linkedon filenot materializedderived fallback50 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
The Global Temporal Retriever (GTR) enhances any forecasting model's accuracy by capturing long-term periodic patterns with minimal computational costs. This limitation prevents them from effectively capturing global periodic patterns that often span cycles significantly longer than the input horizon - despite such patterns carrying strong predictive signals.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Multivariate time series forecasting (MTSF) plays a vital role in numerous real-world applications, yet existing models remain constrained by their reliance on a limited historical context. This limitation prevents them from effectively capturing global periodic patterns that often span cycles significantly longer than the input horizon - despite such patterns carrying strong predictive signals.
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. Extensive experiments on six real-world datasets demonstrate that GTR consistently delivers state-of-the-art performance across both short-term and long-term forecasting scenarios, while incurring minimal parameter and computational overhead.
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

The Global Temporal Retriever (GTR) enhances any forecasting model's accuracy by capturing long-term periodic patterns with minimal computational costs.

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 2602.10847 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(50)

SparseTSF: Lightweight and Robust Time Series Forecasting via Sparse Modeling

2025Shengsheng Lin, Ieee Weiwei Lin Senior Member et al.

Variational Hierarchical N-BEATS Model for Long-Term Time-Series Forecasting

2025Runze Yang, Longbing Cao et al.

CrossLinear: Plug-and-Play Cross-Correlation Embedding for Time Series Forecasting with Exogenous Variables

2025Pengfei Zhou, Yunlong Liu et al.

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

2025Xiaowen Ma, Zhenliang Ni et al.

Temporal Query Network for Efficient Multivariate Time Series Forecasting

2025Shengsheng Lin, Haojun Chen et al.

Efficiently Enhancing Long-term Series Forecasting via Ultra-long Lookback Windows

2025Suxin Tong, Jingling Yuan

Times2D: Multi-Period Decomposition and Derivative Mapping for General Time Series Forecasting

2025Reza Nematirad, Anil Pahwa et al.

TS-RAG: Retrieval-Augmented Generation based Time Series Foundation Models are Stronger Zero-Shot Forecaster

2025Kanghui Ning, Zijie Pan et al.

TimeRAG: Boosting LLM Time Series Forecasting via Retrieval-Augmented Generation

2024Si-Nan Yang, Dong Wang et al.

DUET: Dual Clustering Enhanced Multivariate Time Series Forecasting

2024Xiangfei Qiu, Xingjian Wu et al.

Retrieval Augmented Time Series Forecasting

2024Kutay Tire, Ege Onur Taga et al.

FilterNet: Harnessing Frequency Filters for Time Series Forecasting

2024Kun Yi, Jingru Fei et al.

Retrieval-Augmented Diffusion Models for Time Series Forecasting

2024Jingwei Liu, Ling Yang et al.

TimeMixer++: A General Time Series Pattern Machine for Universal Predictive Analysis

2024Shiyu Wang, Jiawei Li et al.

Rethinking the Power of Timestamps for Robust Time Series Forecasting: A Global-Local Fusion Perspective

2024Chengsen Wang, Qi Qi et al.

CycleNet: Enhancing Time Series Forecasting through Modeling Periodic Patterns

2024Shengsheng Lin, Weiwei Lin et al.

Multiscale Representation Enhanced Temporal Flow Fusion Model for Long-Term Workload Forecasting

2024Shiyu Wang, Zhixuan Chu et al.

TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting

2024Shiyu Wang, Haixu Wu et al.

MDCNet: Long-term time series forecasting with mode decomposition and 2D convolution

2024Jing Su, Dirui Xie et al.

SOFTS: Efficient Multivariate Time Series Forecasting with Series-Core Fusion

2024Lu Han, Xu-Yang Chen et al.

Showing 20 of 50 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 WorkPruning for Generalization: A Transfer-Oriented Spatiotemporal Graph Framework

7.0

Prior WorkHierarchical Spatial-Temporal Graph-Enhanced Model for Map-Matching

7.0

Prior WorkDrift-Resilient Temporal Priors for Visual Tracking

7.0

Extension

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

0.0

Builds On ThisTRACE: A Temporal Conditional Estimation for Multimodal Time Series Foundation Models

0.0

Builds On ThisFrom Text to Forecasts: Bridging Modality Gap with Temporal Evolution Semantic Space

5.0

Commercially relevant

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

8.0

Higher ViabilityRetrieval-Augmented Generation with Covariate Time Series

8.0

Conflicting

Competing ApproachLeapTS: Rethinking Time Series Forecasting as Adaptive Multi-Horizon Scheduling

7.0

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

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: 2602.10847
Route: /paper/enhancing-multivariate-time-series-forecasting-with-global-temporal-retrieval
Active tab: read
Artifact: enhancing-multivariate-time-series-forecasting-with-global-temporal-retrieval

Available agents

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

API/MCP endpoints

REST paper pack API/api/v1/paper/enhancing-multivariate-time-series-forecasting-with-global-temporal-retrieval/paper-pack
REST build passport API/api/v1/paper/enhancing-multivariate-time-series-forecasting-with-global-temporal-retrieval/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/enhancing-multivariate-time-series-forecasting-with-global-temporal-retrieval

stale

Proof freshness: stale
Proof status: unverified
Display score: 7/10
Last proof check: 2026-03-19
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 33%

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

Enhancing Multivariate Time Series Forecasting with Global Temporal Retrieval

Canonical ID enhancing-multivariate-time-series-forecasting-with-global-temporal-retrieval | Route /paper/enhancing-multivariate-time-series-forecasting-with-global-temporal-retrieval

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/enhancing-multivariate-time-series-forecasting-with-global-temporal-retrieval

MCP example

{
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  "arguments": {
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  }
}

source_context

{
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  "mode": "paper",
  "query": "Enhancing Multivariate Time Series Forecasting with Global Temporal Retrieval",
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  "topic_slug": null,
  "benchmark_ref": null,
  "dataset_ref": null
}

Buildability Receipt

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

Paper proof page receipt window

Watch and verify: Enhancing Multivariate Time Series Forecasting with Global Temporal Retrieval

/buildability/enhancing-multivariate-time-series-forecasting-with-global-temporal-retrieval

Watchwatch

Subject: Enhancing Multivariate Time Series Forecasting with Global Temporal Retrieval

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/enhancing-multivariate-time-series-forecasting-with-global-temporal-retrieval

Paper ref

enhancing-multivariate-time-series-forecasting-with-global-temporal-retrieval

arXiv id

2602.10847

Freshness

Generated at

2026-03-19T18:48:05.835Z

Evidence freshness

stale

Last verification

2026-03-19T18:48:05.835Z

Sources

References

Coverage

33%

Hash state

Lineage hash

254b8e313b51c59187e5b909869d6fef1e84164eb73d7cec5b6a57cde87cc9db

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: distribution_readiness_scores
Missing: paper_extraction_scorecards
Unknown: distribution readiness has not been computed 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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Enhancing Multivariate Time Series Forecasting with Global Temporal Retrieval

Enhancing Multivariate Time Series Forecasting with Global Temporal Retrieval

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Brief

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Scientific founder

Translational engineer

Domain operator

GTM lead

Regulatory/clinical advisor

Timeline