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pith:22CCPLGI

pith:2026:22CCPLGI22JYONG2JHZZNVPJTS
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ECTO: Exogenous-Conditioned Temporal Operator for Ultra-Short-Term Wind Power Forecasting

Cao Yuan, Junjun Wang

ECTO improves wind power forecasts by adaptively selecting key meteorological variables with physical priors and refining outputs through regime-specific experts.

arxiv:2605.12196 v2 · 2026-05-12 · cs.LG

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4 Citations open
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Claims

C1strongest claim

ECTO achieves the lowest MSE across all sites, with relative improvements over the strongest baseline ranging from 2.2% to 5.2%, widening to 8.6% at the longer prediction horizon (H=32). Ablation analysis confirms that each exogenous-related component contributes positively (PGVS +1.84%, ECRR +2.86%).

C2weakest assumption

That the hierarchical, group-aware sparse selection in PGVS using domain-informed physical prior generalizes well and that the mixture-of-experts in ECRR learns meaningful regimes without overfitting to the training data.

C3one line summary

ECTO improves ultra-short-term wind power forecasting by 2.2-8.6% using physically-informed variable selection and regime-specific corrections.

Receipt and verification
First computed 2026-05-21T01:04:27.785470Z
Builder pith-number-builder-2026-05-17-v1
Signature Pith Ed25519 (pith-v1-2026-05) · public key
Schema pith-number/v1.0

Canonical hash

d68427acc8d6938734da49f396d5e99ca6651d1e60c175d1a6dc64baff411a2d

Aliases

arxiv: 2605.12196 · arxiv_version: 2605.12196v2 · doi: 10.48550/arxiv.2605.12196 · pith_short_12: 22CCPLGI22JY · pith_short_16: 22CCPLGI22JYONG2 · pith_short_8: 22CCPLGI
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Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/22CCPLGI22JYONG2JHZZNVPJTS \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: d68427acc8d6938734da49f396d5e99ca6651d1e60c175d1a6dc64baff411a2d
Canonical record JSON
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    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2026-05-12T14:37:23Z",
    "title_canon_sha256": "948bb5f2ffd332dd5d25ee7e33e289ad07c22320eb6785d1fb03210350718138"
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