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Zero-shot findings concern held-out datasets, not universal calibration or guaranteed domain transfer. Later Chronos-Bolt direct quantile prediction and Chronos2 covariate/multivariate changes are not part of this original record.", "domain": "Pretrained time-series forecasting" }, "sources": [ { "citation": "Chronos: Learning the Language of Time Series", "url": "https://arxiv.org/abs/2403.07815v1" }, { "citation": "Ansari et al.: Chronos quantized-token probabilistic forecasting — author/institution implementation", "url": "https://github.com/amazon-science/chronos-forecasting" } ], "statement": "# Ansari et al.: Chronos quantized-token probabilistic forecasting\n\nScaled and quantized time-series values into a fixed token vocabulary, pretrained language-model architectures with cross-entropy and sampled future trajectories for probabilistic forecasts; supplemented public data with Gaussian-process synthetic series.\n\n## Scientific scope\n\nT5 and probabilistic forecasting are inherited. Zero-shot findings concern held-out datasets, not universal calibration or guaranteed domain transfer. Later Chronos-Bolt direct quantile prediction and Chronos2 covariate/multivariate changes are not part of this original record.\n\n## Reading status\n\nThis is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.\n\n## People and systems\n\nAbdul Fatir Ansari; Lorenzo Stella; Caner Turkmen; Xiyuan Zhang; Pedro Mercado; Huibin Shen; Oleksandr Shchur; Syama Sundar Rangapuram; Sebastian Pineda Arango; Shubham Kapoor; Jasper Zschiegner; Danielle C. Maddix; Michael W. Mahoney; Kari Torkkola; Andrew Gordon Wilson; Michael Bohlke-Schneider; Yuyang Wang\n\nSource-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.\n\n\n\n## Original sources\n\n- [Chronos: Learning the Language of Time Series](https://arxiv.org/abs/2403.07815v1)\n- [Ansari et al.: Chronos quantized-token probabilistic forecasting — author/institution implementation](https://github.com/amazon-science/chronos-forecasting)\n", "summary": "Scaled and quantized time-series values into a fixed token vocabulary, pretrained language-model architectures with cross-entropy and sampled future trajectories for probabilistic forecasts; supplemented public data with Gaussian-process synthetic series.", "title": "Ansari et al.: Chronos quantized-token probabilistic forecasting" }, "record_id": "urn:uuid:d6805037-640c-4b8b-a527-6a0b54dcb7c0", "version": 1, "source_ref": { "record_id": "urn:uuid:d6805037-640c-4b8b-a527-6a0b54dcb7c0", "version": 1, "content_hash": "sha256:2b999f130a5dcc6e2034f5e0851388b0f4f66736db712300b24096f3af190eec" }, "export_view": "wrs-public-editorial-view-1", "public_view_hash": "sha256:09e0db4f9e2bd7a719ffc7ff16967378b8ea4de360f2677c1fff2fee16e95ef6" }, "target": { "record_id": "urn:uuid:d6805037-640c-4b8b-a527-6a0b54dcb7c0", "version": 1, "content_hash": "sha256:2b999f130a5dcc6e2034f5e0851388b0f4f66736db712300b24096f3af190eec" }, "published_grade": { "profile": "wrs-published-grade-1", "edition": "2026-10-28.1", "id": "ansari-et-al-chronos-quantized-forecasting-2024", "entry_id": "ansari-et-al-chronos-quantized-forecasting-2024", "revision": 1, "title": "Ansari et al.: Chronos quantized-token probabilistic forecasting", "context": "Pretrained time-series forecasting", "track": "Human-designed research method or resource", "band": "B", "modifier": "+", "status": "PROVISIONAL_EDITORIAL", "assessed_on": "2026-10-16", "evidence_cutoff": "2026-10-16", "achievement_state": "SUPPORTED_SCOPED_RESULT", "what_changed": "Scaled and quantized time-series values into a fixed token vocabulary, pretrained language-model architectures with cross-entropy and sampled future trajectories for probabilistic forecasts; supplemented public data with Gaussian-process synthetic series.", "scope": "Scaled and quantized time-series values into a fixed token vocabulary, pretrained language-model architectures with cross-entropy and sampled future trajectories for probabilistic forecasts; supplemented public data with Gaussian-process synthetic series. T5 and probabilistic forecasting are inherited. Zero-shot findings concern held-out datasets, not universal calibration or guaranteed domain transfer. Later Chronos-Bolt direct quantile prediction and Chronos2 covariate/multivariate changes are not part of this original record.", "evidence_summary": "The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. T5 and probabilistic forecasting are inherited. Zero-shot findings concern held-out datasets, not universal calibration or guaranteed domain transfer. Later Chronos-Bolt direct quantile prediction and Chronos2 covariate/multivariate changes are not part of this original record.", "references": [ { "title": "Chronos: Learning the Language of Time Series", "url": "https://arxiv.org/abs/2403.07815v1", "locator": "Original contribution and stated assumptions, evaluation scope and limitations; source-linked assessment, not independent execution or proof certification." }, { "title": "Ansari et al.: Chronos quantized-token probabilistic forecasting — author/institution implementation", "url": "https://github.com/amazon-science/chronos-forecasting", "locator": "Original contribution and stated assumptions, evaluation scope and limitations; source-linked assessment, not independent execution or proof certification." } ], "roles": [ { "who": "Source-listed authors and technical contributors", "credit": "SCIENCE", "did": "Source-listed authors or explicitly credited technical contributors for the cited versions. 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