ARXIV:2603.04873 · 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

SEA-TS: Self-Evolving Agent for Autonomous Code Generation of Time Series Forecasting Algorithms

Q: What is the startup potential of "SEA-TS: Self-Evolving Agent for Autonomous Code Generation o"?

Develop an autonomous system for time series forecasting that optimizes algorithm code using a self-evolving framework.

Q: What products could be built from this research?

To productize this, create an API or cloud service that allows companies to input their data and receive optimized forecasting models, especially in rapidly changing or data-scarce environments.

Q: What are the practical use cases?

An SaaS platform for energy companies that automatically adapts its forecasting models to changing conditions such as weather, consumption patterns, and equipment changes, reducing the need for in-house data science efforts.

Q: What industries could this research disrupt?

This framework could replace traditional time series forecasting approaches that rely heavily on manual intervention and domain expertise, offering a more automated and adaptive solution.

arXiv

SEA-TS autonomously generates and optimizes time series forecasting algorithms with significant performance improvements over existing methods.

Blocked on Code›Score7.0Evidence unverified

Opportunity summary

Pain SEA-TS autonomously generates and optimizes time series forecasting algorithms with significant performance improvements over existing methods.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

SEA-TS autonomously generates and optimizes time series forecasting algorithms with significant performance improvements over existing methods. We propose Self-Evolving Agent for Time Series Algorithms (SEA-TS), a framework that autonomously generates, validates, and optimizes forecasting…

METHOD

Full abstract

Accurate time series forecasting underpins decision-making across domains, yet conventional ML development suffers from data scarcity in new deployments, poor adaptability under distribution shift, and diminishing returns from manual iteration. We propose Self-Evolving Agent for Time Series Algorithms (SEA-TS), a framework that autonomously generates, validates, and optimizes forecasting code via an iterative self-evolution loop. Our framework introduces three key innovations: (1) Metric-Advantage Monte Carlo Tree Search (MA-MCTS), which replaces fixed rewards with a normalized advantage score for discriminative search guidance; (2) Code Review with running prompt refinement, where each executed solution undergoes automated review followed by prompt updates that encode corrective patterns, preventing recurrence of similar errors; and (3) Global Steerable Reasoning, which compares each node against global best and worst solutions, enabling cross-trajectory knowledge transfer. We adopt a MAP-Elites archive for architectural diversity. On the public Solar-Energy benchmark, SEA-TS generated code achieves a 40% MAE reduction relative to TimeMixer, surpassing state-of-the-art methods. On proprietary datasets, SEA-TS generated code reduces WAPE by 8.6% on solar PV forecasting and 7.7% on residential load forecasting compared to human-engineered baselines, and achieves 26.17% MAPE on load forecasting versus 29.34% by TimeMixer. Notably, the evolved models discover novel architectural patterns--including physics-informed monotonic decay heads encoding solar irradiance constraints, per-station learned diurnal cycle profiles, and learnable hourly bias correction--demonstrating that autonomous ML engineering can generate genuinely novel algorithmic ideas beyond manual design.

RESULT

ScienceToStartup currently rates this 7.0/10 on the public viability pass. On the public Solar-Energy benchmark, SEA-TS generated code achieves a 40% MAE reduction relative to TimeMixer, surpassing state-of-the-art methods.

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

PainSEA-TS autonomously generates and optimizes time series forecasting algorithms with significant performance improvements over existing methods.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

SEA-TS autonomously generates and optimizes time series forecasting algorithms with significant performance improvements over existing methods.

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

ARXIV:2603.04873 · 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

SEA-TS: Self-Evolving Agent for Autonomous Code Generation of Time Series Forecasting Algorithms

arXiv

SEA-TS autonomously generates and optimizes time series forecasting algorithms with significant performance improvements over existing methods.

Blocked on Code›Score7.0Evidence unverified

Opportunity summary

Pain SEA-TS autonomously generates and optimizes time series forecasting algorithms with significant performance improvements over existing methods.

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

PainSEA-TS autonomously generates and optimizes time series forecasting algorithms with significant performance improvements over existing methods.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

SEA-TS autonomously generates and optimizes time series forecasting algorithms with significant performance improvements over existing methods.

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

Paper Pack

10.48550/arXiv.2603.04873

SEA-TS: Self-Evolving Agent for Autonomous Code Generation of Time Series Forecasting Algorithms

SEA-TS autonomously generates and optimizes time series forecasting algorithms with significant performance improvements over existing methods.

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 fallback30 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
SEA-TS autonomously generates and optimizes time series forecasting algorithms with significant performance improvements over existing methods. We propose Self-Evolving Agent for Time Series Algorithms (SEA-TS), a framework that autonomously generates, validates, and optimizes forecasting code via an iterative self-evolution loop.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Accurate time series forecasting underpins decision-making across domains, yet conventional ML development suffers from data scarcity in new deployments, poor adaptability under distribution shift, and diminishing returns from manual iteration. We propose Self-Evolving Agent for Time Series Algorithms (SEA-TS), a framework that autonomously generates, validates, and optimizes forecasting code via an iterative self-evolution loop.
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. On the public Solar-Energy benchmark, SEA-TS generated code achieves a 40% MAE reduction relative to TimeMixer, surpassing state-of-the-art methods.
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

SEA-TS autonomously generates and optimizes time series forecasting algorithms with significant performance improvements over existing methods.

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 2603.04873 yet. That is a visibility signal, not a blank module: the monitor is watching the public channels below.

Hacker News

Not indexed yet

Bluesky

Not indexed yet

PDF

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

References(30)

SWE-Pruner: Self-Adaptive Context Pruning for Coding Agents

2026Yuhang Wang, Yuling Shi et al.

Toward Ultra-Long-Horizon Agentic Science: Cognitive Accumulation for Machine Learning Engineering

2026Xinyu Zhu, Yuzhu Cai et al.

More with Less: An Empirical Study of Turn-Control Strategies for Efficient Coding Agents

2025Pengfei Gao, Chao Peng

SciMaster: Towards General-Purpose Scientific AI Agents, Part I. X-Master as Foundation: Can We Lead on Humanity's Last Exam?

2025Jingyi Chai, Shuo Tang et al.

ML-Master: Towards AI-for-AI via Integration of Exploration and Reasoning

2025Zexi Liu, Yuzhu Cai et al.

AlphaEvolve: A coding agent for scientific and algorithmic discovery

2025Alexander Novikov, Ngân V˜u et al.

R&D-Agent: An LLM-Agent Framework Towards Autonomous Data Science

2025Xu Yang, Xiao Yang et al.

I-MCTS: Enhancing Agentic AutoML via Introspective Monte Carlo Tree Search

2025Zujie Liang, Feng Wei et al.

MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering

2024Jun Shern Chan, Neil Chowdhury et al.

TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting

2024Shiyu Wang, Haixu Wu et al.

Timer: Generative Pre-trained Transformers Are Large Time Series Models

2024Yong Liu, Haoran Zhang et al.

MLAgentBench: Evaluating Language Agents on Machine Learning Experimentation

2023Qian Huang, Jian Vora et al.

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

2022Yuqi Nie, Nam H. Nguyen et al.

Are Transformers Effective for Time Series Forecasting?

2022Ailing Zeng, Mu-Hwa Chen et al.

Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting

2021Haixu Wu, Jiehui Xu et al.

MLP-Mixer: An all-MLP Architecture for Vision

2021I. Tolstikhin, N. Houlsby et al.

AutoML: A Survey of the State-of-the-Art

2019Xin He, Kaiyong Zhao et al.

Deep Learning for Household Load Forecasting—A Novel Pooling Deep RNN

2018Heng Shi, Minghao Xu et al.

DARTS: Differentiable Architecture Search

2018Hanxiao Liu, K. Simonyan et al.

Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges

2018Yi Wang, Qixin Chen et al.

Showing 20 of 30 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 WorkTurning Time Series into Algebraic Equations: Symbolic Machine Learning for Interpretable Modeling of Chaotic Time Series

7.0

Prior WorkSEARL: Joint Optimization of Policy and Tool Graph Memory for Self-Evolving Agents

7.0

Prior WorkTMAS: Scaling Test-Time Compute via Multi-Agent Synergy

7.0

Extension

Builds On ThisSeeking SOTA: Time-Series Forecasting Must Adopt Taxonomy-Specific Evaluation to Dispel Illusory Gains

3.0

Builds On ThisVisual Reasoning over Time Series via Multi-Agent System

5.0

Builds On ThisAME-TS: Anchored Mixture-of-Experts for Time Series Forecasting

0.0

Builds On ThisSearth Transformer: A Transformer Architecture Incorporating Earth's Geospheric Physical Priors for Global Mid-Range Weather Forecasting

3.0

Commercially relevant

Higher ViabilitySEA-Eval: A Benchmark for Evaluating Self-Evolving Agents Beyond Episodic Assessment

8.0

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

8.0

Conflicting

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

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

Subscribe to the weekly brief

Get the weekly shortlist of commercializable papers, benchmark movers, and proof receipts that matter for product execution.

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: 2603.04873
Route: /paper/sea-ts-self-evolving-agent-for-autonomous-code-generation-of-time-series-forecasting-algorithms
Active tab: read
Artifact: sea-ts-self-evolving-agent-for-autonomous-code-generation-of-time-series-forecasting-algorithms

Available agents

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

API/MCP endpoints

REST paper pack API/api/v1/paper/sea-ts-self-evolving-agent-for-autonomous-code-generation-of-time-series-forecasting-algorithms/paper-pack
REST build passport API/api/v1/paper/sea-ts-self-evolving-agent-for-autonomous-code-generation-of-time-series-forecasting-algorithms/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

{
  "contract_version": "paper-r2",
  "paper_id": "d297a7f4-bd89-4fa6-ab0f-06a57b448eab",
  "arxiv_id": "2603.04873",
  "canonical_route": "/paper/sea-ts-self-evolving-agent-for-autonomous-code-generation-of-time-series-forecasting-algorithms",
  "active_tab": "synced from current hash by the drawer client",
  "selected_artifact": "sea-ts-self-evolving-agent-for-autonomous-code-generation-of-time-series-forecasting-algorithms",
  "endpoints": {
    "paper_pack": "/api/v1/paper/sea-ts-self-evolving-agent-for-autonomous-code-generation-of-time-series-forecasting-algorithms/paper-pack",
    "build_passport": "/api/v1/paper/sea-ts-self-evolving-agent-for-autonomous-code-generation-of-time-series-forecasting-algorithms/build-passport",
    "mcp_resource": "sciencetostartup://surfaces/paper-workspace"
  }
}

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/sea-ts-self-evolving-agent-for-autonomous-code-generation-of-time-series-forecasting-algorithms

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

SEA-TS: Self-Evolving Agent for Autonomous Code Generation of Time Series Forecasting Algorithms

Canonical ID sea-ts-self-evolving-agent-for-autonomous-code-generation-of-time-series-forecasting-algorithms | Route /paper/sea-ts-self-evolving-agent-for-autonomous-code-generation-of-time-series-forecasting-algorithms

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/sea-ts-self-evolving-agent-for-autonomous-code-generation-of-time-series-forecasting-algorithms

MCP example

{
  "tool": "get_paper",
  "arguments": {
    "arxiv_id": "2603.04873"
  }
}

source_context

{
  "surface": "paper",
  "mode": "paper",
  "query": "SEA-TS: Self-Evolving Agent for Autonomous Code Generation of Time Series Forecasting Algorithms",
  "normalized_query": "2603.04873",
  "route": "/paper/sea-ts-self-evolving-agent-for-autonomous-code-generation-of-time-series-forecasting-algorithms",
  "paper_ref": "sea-ts-self-evolving-agent-for-autonomous-code-generation-of-time-series-forecasting-algorithms",
  "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: SEA-TS: Self-Evolving Agent for Autonomous Code Generation of Time Series Forecasting Algorithms

/buildability/sea-ts-self-evolving-agent-for-autonomous-code-generation-of-time-series-forecasting-algorithms

Watchwatch

Subject: SEA-TS: Self-Evolving Agent for Autonomous Code Generation of Time Series Forecasting Algorithms

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/sea-ts-self-evolving-agent-for-autonomous-code-generation-of-time-series-forecasting-algorithms

Paper ref

sea-ts-self-evolving-agent-for-autonomous-code-generation-of-time-series-forecasting-algorithms

arXiv id

2603.04873

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

991ce52c7a0571998a48428538d7cbfc4765652dc874f22dcf4dc632350cfc08

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.

{
  "@context": "https://schema.org",
  "@graph": [
    {
      "@type": "WebPage",
      "@id": "https://sciencetostartup.com/paper/sea-ts-self-evolving-agent-for-autonomous-code-generation-of-time-series-forecasting-algorithms#webpage",
      "url": "https://sciencetostartup.com/paper/sea-ts-self-evolving-agent-for-autonomous-code-generation-of-time-series-forecasting-algorithms",
      "name": "SEA-TS: Self-Evolving Agent for Autonomous Code Generation of Time Series Forecasting Algorithms",
      "description": "SEA-TS autonomously generates and optimizes time series forecasting algorithms with significant performance improvements over existing methods.",
      "isPartOf": {
        "@id": "https://sciencetostartup.com/#website"
      }
    },
    {
      "@type": "ScholarlyArticle",
      "@id": "https://sciencetostartup.com/paper/sea-ts-self-evolving-agent-for-autonomous-code-generation-of-time-series-forecasting-algorithms#scholarlyArticle",
      "headline": "SEA-TS: Self-Evolving Agent for Autonomous Code Generation of Time Series Forecasting Algorithms",
      "description": "SEA-TS autonomously generates and optimizes time series forecasting algorithms with significant performance improvements over existing methods.",
      "url": "https://sciencetostartup.com/paper/sea-ts-self-evolving-agent-for-autonomous-code-generation-of-time-series-forecasting-algorithms",
      "sameAs": "https://arxiv.org/abs/2603.04873",
      "identifier": {
        "@type": "PropertyValue",
        "propertyID": "arXiv",
        "value": "2603.04873"
      },
      "isAccessibleForFree": true,
      "isPartOf": {
        "@id": "https://sciencetostartup.com/#website"
      },
      "datePublished": "2026-03-05T07:02:17.000Z",
      "author": [
        {
          "@type": "Person",
          "name": "Longkun Xu",
          "affiliation": {
            "@type": "Organization",
            "name": "EcoFlow Inc., Shenzhen, China"
          }
        },
        {
          "@type": "Person",
          "name": "Xiaochun Zhang",
          "affiliation": {
            "@type": "Organization",
            "name": "EcoFlow Inc., Shenzhen, China"
          }
        },
        {
          "@type": "Person",
          "name": "Qiantu Tuo",
          "affiliation": {
            "@type": "Organization",
            "name": "EcoFlow Inc., Shenzhen, China"
          }
        },
        {
          "@type": "Person",
          "name": "Rui Li",
          "affiliation": {
            "@type": "Organization",
            "name": "EcoFlow Inc., Shenzhen, China"
          }
        }
      ],
      "citation": [
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "dacac5766fc96ca71a2a48590bbe6673e4360b88"
          },
          "url": "https://www.semanticscholar.org/paper/dacac5766fc96ca71a2a48590bbe6673e4360b88"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "960fb2ff6bc20697a0ca16d65e9e95c5b57a118b"
          },
          "url": "https://www.semanticscholar.org/paper/960fb2ff6bc20697a0ca16d65e9e95c5b57a118b"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "7671bdcde6687cdbe3918e6ff35aa380f958c556"
          },
          "url": "https://www.semanticscholar.org/paper/7671bdcde6687cdbe3918e6ff35aa380f958c556"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "fb0e61daa06faa107811a8fa43cf62ed2a9cd32a"
          },
          "url": "https://www.semanticscholar.org/paper/fb0e61daa06faa107811a8fa43cf62ed2a9cd32a"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "363aaf46abbd4a7d48b48cdcbaf401508cd7f298"
          },
          "url": "https://www.semanticscholar.org/paper/363aaf46abbd4a7d48b48cdcbaf401508cd7f298"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "ed32e5bb11a9e5e2f03ea805782e8670a6e10efd"
          },
          "url": "https://www.semanticscholar.org/paper/ed32e5bb11a9e5e2f03ea805782e8670a6e10efd"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "c351338ab8cbf15499f39af121b5c20a896ab92b"
          },
          "url": "https://www.semanticscholar.org/paper/c351338ab8cbf15499f39af121b5c20a896ab92b"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "19f0744537e078d830fcee56e64589ff38c9411e"
          },
          "url": "https://www.semanticscholar.org/paper/19f0744537e078d830fcee56e64589ff38c9411e"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "7c44b7fdcec2e517799f6c54f6ba42bf1a89d2e6"
          },
          "url": "https://www.semanticscholar.org/paper/7c44b7fdcec2e517799f6c54f6ba42bf1a89d2e6"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "06353b9112ab14c26ce9d3c851c01ebe4b798177"
          },
          "url": "https://www.semanticscholar.org/paper/06353b9112ab14c26ce9d3c851c01ebe4b798177"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "73f58b90697f957832f5090946894480849dea3a"
          },
          "url": "https://www.semanticscholar.org/paper/73f58b90697f957832f5090946894480849dea3a"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "b8ee0b5322382807e687c95cc87b059d3f348495"
          },
          "url": "https://www.semanticscholar.org/paper/b8ee0b5322382807e687c95cc87b059d3f348495"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "dad15404d372a23b4b3bf9a63b3124693df3c85e"
          },
          "url": "https://www.semanticscholar.org/paper/dad15404d372a23b4b3bf9a63b3124693df3c85e"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "5f404dbba07619cc7f28d75d03f124a52290046e"
          },
          "url": "https://www.semanticscholar.org/paper/5f404dbba07619cc7f28d75d03f124a52290046e"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "fc46ccb83dc121c33de7ab6bdedab7d970780b2f"
          },
          "url": "https://www.semanticscholar.org/paper/fc46ccb83dc121c33de7ab6bdedab7d970780b2f"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "67571d29190faea9fbd104acd16274f8c4edf254"
          },
          "url": "https://www.semanticscholar.org/paper/67571d29190faea9fbd104acd16274f8c4edf254"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "3a7fa673ff8ec4ec2f322473de005f3cd09ea820"
          },
          "url": "https://www.semanticscholar.org/paper/3a7fa673ff8ec4ec2f322473de005f3cd09ea820"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "d140fb7f9537c309e50845dd6cd06a4482ac57c5"
          },
          "url": "https://www.semanticscholar.org/paper/d140fb7f9537c309e50845dd6cd06a4482ac57c5"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "c1f457e31b611da727f9aef76c283a18157dfa83"
          },
          "url": "https://www.semanticscholar.org/paper/c1f457e31b611da727f9aef76c283a18157dfa83"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "4ffbd6860cc2cada1f4c6c77ad3f9b6535c4b3cf"
          },
          "url": "https://www.semanticscholar.org/paper/4ffbd6860cc2cada1f4c6c77ad3f9b6535c4b3cf"
        }
      ],
      "additionalProperty": [
        {
          "@type": "PropertyValue",
          "propertyID": "viabilityScore",
          "value": 7
        },
        {
          "@type": "PropertyValue",
          "propertyID": "researchDomain",
          "value": "Time Series Forecasting"
        }
      ]
    },
    {
      "@type": "BreadcrumbList",
      "itemListElement": [
        {
          "@type": "ListItem",
          "position": 1,
          "name": "Home",
          "item": "https://sciencetostartup.com"
        },
        {
          "@type": "ListItem",
          "position": 2,
          "name": "Time Series Forecasting",
          "item": "https://sciencetostartup.com/topics"
        },
        {
          "@type": "ListItem",
          "position": 3,
          "name": "SEA-TS: Self-Evolving Agent for Autonomous Code Generation o",
          "item": "https://sciencetostartup.com/paper/sea-ts-self-evolving-agent-for-autonomous-code-generation-of-time-series-forecasting-algorithms"
        }
      ]
    },
    {
      "@type": "FAQPage",
      "mainEntity": [
        {
          "@type": "Question",
          "name": "What is the startup potential of \"SEA-TS: Self-Evolving Agent for Autonomous Code Generation o\"?",
          "acceptedAnswer": {
            "@type": "Answer",
            "text": "Develop an autonomous system for time series forecasting that optimizes algorithm code using a self-evolving framework."
          }
        },
        {
          "@type": "Question",
          "name": "What products could be built from this research?",
          "acceptedAnswer": {
            "@type": "Answer",
            "text": "To productize this, create an API or cloud service that allows companies to input their data and receive optimized forecasting models, especially in rapidly changing or data-scarce environments."
          }
        },
        {
          "@type": "Question",
          "name": "What are the practical use cases?",
          "acceptedAnswer": {
            "@type": "Answer",
            "text": "An SaaS platform for energy companies that automatically adapts its forecasting models to changing conditions such as weather, consumption patterns, and equipment changes, reducing the need for in-house data science efforts."
          }
        },
        {
          "@type": "Question",
          "name": "What industries could this research disrupt?",
          "acceptedAnswer": {
            "@type": "Answer",
            "text": "This framework could replace traditional time series forecasting approaches that rely heavily on manual intervention and domain expertise, offering a more automated and adaptive solution."
          }
        }
      ]
    }
  ]
}

SEA-TS: Self-Evolving Agent for Autonomous Code Generation of Time Series Forecasting Algorithms

SEA-TS: Self-Evolving Agent for Autonomous Code Generation of Time Series Forecasting Algorithms

Claim map

Constellation map

Competitive landscape

Buzz

PDF

References(30)

Related Papers

Related Resources

Subscribe to the weekly brief

Build artifacts

Brief

Experiment plan

Validation checklist

Scientific founder

Translational engineer

Domain operator

GTM lead

Regulatory/clinical advisor

Timeline

Claim map

Constellation map

Competitive landscape

Buzz

PDF

References(30)

Related Papers

Related Resources

Subscribe to the weekly brief

Build artifacts

Brief

Experiment plan

Validation checklist

Scientific founder

Translational engineer

Domain operator

GTM lead

Regulatory/clinical advisor

Timeline