Pith Number

pith:QPXDRLKK

pith:2025:QPXDRLKKPEWLK6AJKDOFYDWHS4

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Chronos-2: From Univariate to Universal Forecasting

Abdul Fatir Ansari, Andreas Auer, Boran Han, Danielle C. Maddix, George Karypis, Hao Wang, Huibin Shen, Huzefa Rangwala, Jaris K\"uken, Junming Yin, Lorenzo Stella, Michael Bohlke-Schneider, Mononito Goswami, Nick Erickson, Oleksandr Shchur, Pablo Guerron, Pedro Mercado, Prateek Mutalik Desai, Shubham Kapoor, Syama Sundar Rangapuram, Tony Hu, Xiyuan Zhang, Yuyang Wang

Chronos-2 is a pretrained model that performs zero-shot forecasting on univariate, multivariate, and covariate-informed tasks via group attention for in-context learning.

arxiv:2510.15821 v1 · 2025-10-17 · cs.LG · cs.AI · stat.ML

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Record completeness

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3 Author claim open · sign in to claim

4 Citations open

5 Replications open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

Chronos-2 delivers state-of-the-art performance across three comprehensive benchmarks: fev-bench, GIFT-Eval, and Chronos Benchmark II. On fev-bench, which emphasizes multivariate and covariate-informed forecasting, Chronos-2's universal ICL capabilities lead to substantial improvements over existing models. On tasks involving covariates, it consistently outperforms baselines by a wide margin.

C2weakest assumption

That training exclusively on synthetic datasets that impose diverse multivariate structures on univariate series will produce a model whose in-context learning generalizes to real-world multivariate and covariate distributions without domain-specific fine-tuning.

C3one line summary

Chronos-2 adds group attention to a pretrained time series model so it can do zero-shot forecasting on univariate, multivariate, and covariate tasks by learning from synthetic data that imposes multivariate structure on univariate series.

Formal links

2 machine-checked theorem links

Cited by

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Receipt and verification

First computed	2026-05-17T23:39:05.143189Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

83ee38ad4a792cb5780950dc5c0ec797139724552a0557e279747ed440b77eea

Aliases

arxiv: 2510.15821 · arxiv_version: 2510.15821v1 · doi: 10.48550/arxiv.2510.15821 · pith_short_12: QPXDRLKKPEWL · pith_short_16: QPXDRLKKPEWLK6AJ · pith_short_8: QPXDRLKK

Agent API

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/QPXDRLKKPEWLK6AJKDOFYDWHS4 \
  | 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: 83ee38ad4a792cb5780950dc5c0ec797139724552a0557e279747ed440b77eea

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "3b420ee178cd8597525b773210382240a681b46bf9b6cb94e0d80d91e8f47ab3",
    "cross_cats_sorted": [
      "cs.AI",
      "stat.ML"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2025-10-17T17:00:53Z",
    "title_canon_sha256": "28ca181e7696ebac4a0c600f8e9e2e755a69e19d619f88ce76a950491e57ee7b"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2510.15821",
    "kind": "arxiv",
    "version": 1
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}