Pith Number

pith:T726ULAL

pith:2023:T726ULALAK6QWYIEF5PIV6UDCY

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A decoder-only foundation model for time-series forecasting

Abhimanyu Das, Rajat Sen, Weihao Kong, Yichen Zhou

A pretrained decoder-only model achieves zero-shot time-series forecasting accuracy close to supervised state-of-the-art on public datasets.

arxiv:2310.10688 v4 · 2023-10-14 · cs.CL · cs.AI · cs.LG

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4 Citations open

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Claims

C1strongest claim

our model ... whose out-of-the-box zero-shot performance on a variety of public datasets comes close to the accuracy of state-of-the-art supervised forecasting models for each individual dataset.

C2weakest assumption

That pretraining on the chosen large time-series corpus produces representations that generalize to unseen datasets and varying temporal granularities without any fine-tuning or dataset-specific adaptation.

C3one line summary

A pretrained decoder-only patched transformer achieves near state-of-the-art zero-shot forecasting performance across diverse time series datasets and settings.

References

23 extracted · 23 resolved · 7 Pith anchors

[1] On the benefits of maximum likelihood estimation for regression and forecasting

[2] Conditional time series forecast- ing with convolutional neural networks · arXiv:1703.04691

[3] Tsmixer: An all-mlp architecture for time series forecasting

[4] [COO+23] Cristian Challu, Kin G. Olivares, Boris N. Oreshkin, Federico Garza, Max Mergenthaler, and Artur Dubrawski. NHITS: Neural Hierarchical Interpolation for Time Series forecasting. In The Associ 2023

[5] Llm4ts: Two-stage fine-tuning for time-series forecasting with pre-trained llms

Formal links

3 machine-checked theorem links

Cited by

26 papers in Pith

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ChronoVAE-HOPE: Beyond Attention -- A Next-Generation VAE Foundation Model for Specialized Time Series Classification

TelecomTS: A Multi-Modal Observability Dataset for Time Series and Language Analysis

KairosHope: A Next-Generation Time-Series Foundation Model for Specialized Classification via Dual-Memory Architecture

Quantifying the Pre-training Dividend: Generative versus Latent Self-Supervised Learning for Time Series Foundation Models

Receipt and verification

First computed	2026-05-17T23:38:47.021386Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

9ff5ea2c0b02bd0b61042f5e8afa8316023273bf9274d3ff0aaee651b7478ffc

Aliases

arxiv: 2310.10688 · arxiv_version: 2310.10688v4 · doi: 10.48550/arxiv.2310.10688 · pith_short_12: T726ULALAK6Q · pith_short_16: T726ULALAK6QWYIE · pith_short_8: T726ULAL

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/T726ULALAK6QWYIEF5PIV6UDCY \
  | 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: 9ff5ea2c0b02bd0b61042f5e8afa8316023273bf9274d3ff0aaee651b7478ffc

Canonical record JSON

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    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.CL",
    "submitted_at": "2023-10-14T17:01:37Z",
    "title_canon_sha256": "31a7eeb94075a0e05297d422c2bed9f02bf491c86a716ee0d88d6c0f99fe6f79"
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