{"as_of":"2026-08-10T20:46:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c407514341b4cd1c43783f4e8eb7013d29e2ec24e0124007bd4c1fb5ecc081c3","coverage":[{"denominator":36,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":36,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T11:30:15.260931Z","state":"measured"},{"denominator":36,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":36,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2506.02389/citation-record","integrity":"/paper/2506.02389/integrity","json":"/paper/2506.02389/citation-record.json","paper":"/paper/2506.02389"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2401.02954","last_updated":"2024-01-05T18:59:13Z","snapshot_observed_at":"2026-08-10T17:03:38.042994Z","submitted_at":"2024-01-05T18:59:13Z","title":"DeepSeek LLM: Scaling Open-Source Language Models with Longtermism","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.02954","snapshot_observed_at":"2026-08-07T11:30:12.768875Z","title":"Deepseek llm: Scaling open-source languag e models with longtermism","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.02389","last_updated":"2025-06-03T03:02:47Z","snapshot_observed_at":"2026-08-09T09:03:10.834019Z","submitted_at":"2025-06-03T03:02:47Z","title":"Univariate to Multivariate: LLMs as Zero-Shot Predictors for Time-Series Forecasting","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:12.768875Z"},"links":{"cited_paper":"/paper/2401.02954","citing_paper":"/paper/2506.02389"},"observation_digest":"sha256:ba9e7c0227d7d2298f247f4185c4bf44b06bc478c004bac2a918d3b34d3aea21","observation_id":"c1c2a987-fe09-4fa0-b9fa-e17584e200cc","resolution":{"observed_at":"2026-08-07T11:30:12.768875Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:30:15.510379Z","title":"Llama 3.2: Multilingual large language models","venue":null,"work_id":"bbdefca8-0136-4ef9-9bf7-80d9254308c3","year":2024},"citing_paper":{"arxiv_id":"2506.02389","last_updated":"2025-06-03T03:02:47Z","snapshot_observed_at":"2026-08-09T09:03:10.834019Z","submitted_at":"2025-06-03T03:02:47Z","title":"Univariate to Multivariate: LLMs as Zero-Shot Predictors for Time-Series Forecasting","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:12.819866Z"},"links":{"citing_paper":"/paper/2506.02389"},"observation_digest":"sha256:f6bf9b2a13efa16a9ce9a6780211432970efa48dd02339c7039fbeceef4bab14","observation_id":"10235192-a290-4fd8-9fb4-20aeb46bb1e5","resolution":{"observed_at":"2026-08-07T11:30:15.512676Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:30:15.504068Z","title":"Comparative study on th e effect of order and cut off frequency of butterworth low pass ﬁlter for removal of noise in ecg signal","venue":null,"work_id":"9e8f1a21-a95d-49a6-b725-a622544b2a9f","year":2020},"citing_paper":{"arxiv_id":"2506.02389","last_updated":"2025-06-03T03:02:47Z","snapshot_observed_at":"2026-08-09T09:03:10.834019Z","submitted_at":"2025-06-03T03:02:47Z","title":"Univariate to Multivariate: LLMs as Zero-Shot Predictors for Time-Series Forecasting","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:12.903813Z"},"links":{"citing_paper":"/paper/2506.02389"},"observation_digest":"sha256:1f966c3842c067513b39ef31f15e6f5d97ac027690b461dd497e77799fdbecec","observation_id":"55dd5304-3b8e-49ce-91ea-bb89f2ea8970","resolution":{"observed_at":"2026-08-07T11:30:15.506276Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:30:15.498025Z","title":"Selection of the most suit able decomposition ﬁlter for the mea- surement of ﬂuctuating harmonics","venue":null,"work_id":"304e114b-6b2e-4ee6-9751-db7bc973bb0b","year":2016},"citing_paper":{"arxiv_id":"2506.02389","last_updated":"2025-06-03T03:02:47Z","snapshot_observed_at":"2026-08-09T09:03:10.834019Z","submitted_at":"2025-06-03T03:02:47Z","title":"Univariate to Multivariate: LLMs as Zero-Shot Predictors for Time-Series Forecasting","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:12.958273Z"},"links":{"citing_paper":"/paper/2506.02389"},"observation_digest":"sha256:2528c78dfd30a076ad8eeb14b6b8109625b173a0e91f3db6e037fede2e0d7f19","observation_id":"e0640f7f-98cc-4c1d-b607-e7e723d54972","resolution":{"observed_at":"2026-08-07T11:30:15.500263Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2308.08469","last_updated":"2025-02-20T16:48:08Z","snapshot_observed_at":"2026-08-10T15:16:16.072962Z","submitted_at":"2023-08-16T16:19:50Z","title":"LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.08469","snapshot_observed_at":"2026-08-07T11:30:13.060452Z","title":"Llm4ts: Aligning pre- trained llms as data-efﬁcient time-series forecasters","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.02389","last_updated":"2025-06-03T03:02:47Z","snapshot_observed_at":"2026-08-09T09:03:10.834019Z","submitted_at":"2025-06-03T03:02:47Z","title":"Univariate to Multivariate: LLMs as Zero-Shot Predictors for Time-Series Forecasting","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:13.060452Z"},"links":{"cited_paper":"/paper/2308.08469","citing_paper":"/paper/2506.02389"},"observation_digest":"sha256:ce180d4e8a2a6c3168a3c7e41153bf35019800d38e962f45c25adb4127b16074","observation_id":"194439d7-a13d-49ff-a37d-c7d662b9dadf","resolution":{"observed_at":"2026-08-07T11:30:13.060452Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:30:15.491511Z","title":"Sd- former: Similarity-driven discrete transformer for time s eries generation","venue":null,"work_id":"f3092030-4808-4c79-9924-8e27c8d07254","year":2024},"citing_paper":{"arxiv_id":"2506.02389","last_updated":"2025-06-03T03:02:47Z","snapshot_observed_at":"2026-08-09T09:03:10.834019Z","submitted_at":"2025-06-03T03:02:47Z","title":"Univariate to Multivariate: LLMs as Zero-Shot Predictors for Time-Series Forecasting","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:13.180326Z"},"links":{"citing_paper":"/paper/2506.02389"},"observation_digest":"sha256:5c6c79d563e433f540ddcda4c601154bd19fe2300b6f8764ed47b322df191a26","observation_id":"8dafa29a-793c-4ac8-9df0-767ddcee701a","resolution":{"observed_at":"2026-08-07T11:30:15.493979Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.13857","last_updated":"2025-06-21T09:18:16Z","snapshot_observed_at":"2026-07-06T19:35:30.732080Z","submitted_at":"2024-10-17T17:59:35Z","title":"How Numerical Precision Affects Arithmetical Reasoning Capabilities of LLMs","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.13857","snapshot_observed_at":"2026-08-07T11:30:13.255835Z","title":"How numerical precision affects mathema tical reasoning capabilities of llms","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.02389","last_updated":"2025-06-03T03:02:47Z","snapshot_observed_at":"2026-08-09T09:03:10.834019Z","submitted_at":"2025-06-03T03:02:47Z","title":"Univariate to Multivariate: LLMs as Zero-Shot Predictors for Time-Series Forecasting","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:13.255835Z"},"links":{"cited_paper":"/paper/2410.13857","citing_paper":"/paper/2506.02389"},"observation_digest":"sha256:36457ba685875b1ca212dba9dc3bf7753d607832c994db8465ec66a44e7958cf","observation_id":"6d39dbfb-84c1-4737-ab04-1db9ba0130b2","resolution":{"observed_at":"2026-08-07T11:30:13.255835Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:30:15.485488Z","title":"Deep learning wi th long short-term memory net- works for ﬁnancial market predictions","venue":null,"work_id":"7d41a9bc-9e2c-46a6-9c06-9adbac806a38","year":2018},"citing_paper":{"arxiv_id":"2506.02389","last_updated":"2025-06-03T03:02:47Z","snapshot_observed_at":"2026-08-09T09:03:10.834019Z","submitted_at":"2025-06-03T03:02:47Z","title":"Univariate to Multivariate: LLMs as Zero-Shot Predictors for Time-Series Forecasting","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:13.305318Z"},"links":{"citing_paper":"/paper/2506.02389"},"observation_digest":"sha256:ce59c57161ed049acfe4e972676028d9f6828a03de900fb2ffb941050f74f559","observation_id":"8be00f32-8693-4622-8976-830c56afdbfd","resolution":{"observed_at":"2026-08-07T11:30:15.487580Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:30:15.479068Z","title":"Large language models are zero- shot time series forecasters","venue":null,"work_id":"111fc5bc-90d2-48aa-8d4e-08097ff8627c","year":2023},"citing_paper":{"arxiv_id":"2506.02389","last_updated":"2025-06-03T03:02:47Z","snapshot_observed_at":"2026-08-09T09:03:10.834019Z","submitted_at":"2025-06-03T03:02:47Z","title":"Univariate to Multivariate: LLMs as Zero-Shot Predictors for Time-Series Forecasting","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:13.382563Z"},"links":{"citing_paper":"/paper/2506.02389"},"observation_digest":"sha256:a3ca835e5eff8481c5f90aa7921b5d4aeb8e7fc214d50e85fe562e81664e86f4","observation_id":"01e384a0-752d-481e-9870-33083a9f156b","resolution":{"observed_at":"2026-08-07T11:30:15.481280Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:30:15.472987Z","title":"SOF TS: Efﬁcient multivariate time series forecasting with series-core fusion","venue":null,"work_id":"46d1693d-21ab-49bd-8709-1e48df40e67b","year":2024},"citing_paper":{"arxiv_id":"2506.02389","last_updated":"2025-06-03T03:02:47Z","snapshot_observed_at":"2026-08-09T09:03:10.834019Z","submitted_at":"2025-06-03T03:02:47Z","title":"Univariate to Multivariate: LLMs as Zero-Shot Predictors for Time-Series Forecasting","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:13.455836Z"},"links":{"citing_paper":"/paper/2506.02389"},"observation_digest":"sha256:3385aba95e4d182aeec4360c7c153d8ce4853b7aafb6e053aaaab1179798bc1a","observation_id":"41c9c930-1c47-4bab-8c13-1ca231323021","resolution":{"observed_at":"2026-08-07T11:30:15.475147Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:30:15.466802Z","title":"An intelligent network t rafﬁc prediction method based on butterworth ﬁlter and cnn–lstm","venue":null,"work_id":"8e5edfaa-ad53-47ae-ac67-3fa638eb8d32","year":2024},"citing_paper":{"arxiv_id":"2506.02389","last_updated":"2025-06-03T03:02:47Z","snapshot_observed_at":"2026-08-09T09:03:10.834019Z","submitted_at":"2025-06-03T03:02:47Z","title":"Univariate to Multivariate: LLMs as Zero-Shot Predictors for Time-Series Forecasting","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:13.542913Z"},"links":{"citing_paper":"/paper/2506.02389"},"observation_digest":"sha256:806e21ea407db1053be6b2786731e6744176d924fdb970392a74c4001bb940d8","observation_id":"14f1aeb6-1be8-4470-96dd-d124560118ca","resolution":{"observed_at":"2026-08-07T11:30:15.469153Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.01728","last_updated":"2024-01-29T06:27:53Z","snapshot_observed_at":"2026-08-04T04:31:27.172482Z","submitted_at":"2023-10-03T01:31:25Z","title":"Time-LLM: Time Series Forecasting by Reprogramming Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.01728","snapshot_observed_at":"2026-08-07T11:30:13.608524Z","title":"Time-ll m: Time series forecasting by reprogramming large language models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.02389","last_updated":"2025-06-03T03:02:47Z","snapshot_observed_at":"2026-08-09T09:03:10.834019Z","submitted_at":"2025-06-03T03:02:47Z","title":"Univariate to Multivariate: LLMs as Zero-Shot Predictors for Time-Series Forecasting","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:13.608524Z"},"links":{"cited_paper":"/paper/2310.01728","citing_paper":"/paper/2506.02389"},"observation_digest":"sha256:bfa232f8b9dafe2313521579777c803d1ad6d527d0e61e220db404492110c97d","observation_id":"80f712e8-a49b-463b-b92f-300234d7c75c","resolution":{"observed_at":"2026-08-07T11:30:13.608524Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:30:15.460103Z","title":"Back to basics: The power of the multilayer perceptron in ﬁnancial time series foreca sting","venue":null,"work_id":"19570372-ef8a-4ade-9416-6725d861b2de","year":1920},"citing_paper":{"arxiv_id":"2506.02389","last_updated":"2025-06-03T03:02:47Z","snapshot_observed_at":"2026-08-09T09:03:10.834019Z","submitted_at":"2025-06-03T03:02:47Z","title":"Univariate to Multivariate: LLMs as Zero-Shot Predictors for Time-Series Forecasting","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:13.622733Z"},"links":{"citing_paper":"/paper/2506.02389"},"observation_digest":"sha256:dded063d3d24dfaf7de8db843c9b8c56d1d2ee8371a59a36c1be85baa0a7b081","observation_id":"1c3cadb6-4ff4-4316-9c50-602d3e67c3e2","resolution":{"observed_at":"2026-08-07T11:30:15.462450Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:30:15.453538Z","title":"Autotimes: Au- toregressive time series forecasters via large language models","venue":null,"work_id":"f49108e2-2dbd-496b-9cdc-316af58280e5","year":2024},"citing_paper":{"arxiv_id":"2506.02389","last_updated":"2025-06-03T03:02:47Z","snapshot_observed_at":"2026-08-09T09:03:10.834019Z","submitted_at":"2025-06-03T03:02:47Z","title":"Univariate to Multivariate: LLMs as Zero-Shot Predictors for Time-Series Forecasting","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:13.728515Z"},"links":{"citing_paper":"/paper/2506.02389"},"observation_digest":"sha256:c9c1a8bb1e0d7a34a85a42103f24720a2f34a5449e6d25bf7badf7d5a9723820","observation_id":"64e8aaa9-4a5a-4baf-afca-5997a953ad61","resolution":{"observed_at":"2026-08-07T11:30:15.456216Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:30:15.446751Z","title":"Trafﬁc ﬂow predic- tion with big data: A deep learning approach","venue":null,"work_id":"cba19a9c-2c9a-428b-94ea-b10a6a1bae61","year":2014},"citing_paper":{"arxiv_id":"2506.02389","last_updated":"2025-06-03T03:02:47Z","snapshot_observed_at":"2026-08-09T09:03:10.834019Z","submitted_at":"2025-06-03T03:02:47Z","title":"Univariate to Multivariate: LLMs as Zero-Shot Predictors for Time-Series Forecasting","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:13.875531Z"},"links":{"citing_paper":"/paper/2506.02389"},"observation_digest":"sha256:3d5b8ee0e9e8290c2fe7fd18beeffb6fb560b79e796a463d6dd88bf0f608ef19","observation_id":"d3b3e32e-6ecf-48eb-9360-abb6f03e5cba","resolution":{"observed_at":"2026-08-07T11:30:15.448994Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:30:15.440647Z","title":"Videotrain++: Gan-based adap tive framework for synthetic video trafﬁc generation","venue":null,"work_id":"b27343a2-6bd8-4d84-98c6-20ab69438f7a","year":2022},"citing_paper":{"arxiv_id":"2506.02389","last_updated":"2025-06-03T03:02:47Z","snapshot_observed_at":"2026-08-09T09:03:10.834019Z","submitted_at":"2025-06-03T03:02:47Z","title":"Univariate to Multivariate: LLMs as Zero-Shot Predictors for Time-Series Forecasting","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:14.024285Z"},"links":{"citing_paper":"/paper/2506.02389"},"observation_digest":"sha256:6559f82a5fb1ad7c973a218b89c988123e9635d1bbf2e159ace3506605be85f3","observation_id":"5082bf9e-d801-4151-8dce-bb5aa92b7c91","resolution":{"observed_at":"2026-08-07T11:30:15.442845Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:30:15.434130Z","title":"Gpt-4o mini: Advancing cost-efﬁcient intelli gence","venue":null,"work_id":"ac1c9265-edbf-4fac-8fb3-68937d404a14","year":null},"citing_paper":{"arxiv_id":"2506.02389","last_updated":"2025-06-03T03:02:47Z","snapshot_observed_at":"2026-08-09T09:03:10.834019Z","submitted_at":"2025-06-03T03:02:47Z","title":"Univariate to Multivariate: LLMs as Zero-Shot Predictors for Time-Series Forecasting","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:14.096083Z"},"links":{"citing_paper":"/paper/2506.02389"},"observation_digest":"sha256:dede32ef99edf8683289c9ee74cdb34687fdc3d38bc2f242029f295c5236a7bb","observation_id":"8aa33e96-a780-419d-ad64-c472b15d469a","resolution":{"observed_at":"2026-08-07T11:30:15.436466Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:30:15.421936Z","title":"Fred- former: Frequency debiased transformer for time series for ecasting","venue":null,"work_id":"6c1feac5-1073-4779-adce-d730ada60937","year":2024},"citing_paper":{"arxiv_id":"2506.02389","last_updated":"2025-06-03T03:02:47Z","snapshot_observed_at":"2026-08-09T09:03:10.834019Z","submitted_at":"2025-06-03T03:02:47Z","title":"Univariate to Multivariate: LLMs as Zero-Shot Predictors for Time-Series Forecasting","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:14.356908Z"},"links":{"citing_paper":"/paper/2506.02389"},"observation_digest":"sha256:57ba755ff1ee0e51ac4afdd739c5549d0b5e73048b10ef524216c5c5112c6219","observation_id":"ea455261-450a-44b1-af3f-80d1b4c0117e","resolution":{"observed_at":"2026-08-07T11:30:15.424131Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.00226","last_updated":"2025-03-31T21:06:39Z","snapshot_observed_at":"2026-08-07T16:19:11.837916Z","submitted_at":"2025-03-31T21:06:39Z","title":"Large Language Models in Numberland: A Quick Test of Their Numerical Reasoning Abilities","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.00226","snapshot_observed_at":"2026-08-07T11:30:14.513983Z","title":"Large language models in numberland: A quick test of their numerical reasoning abilities","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.02389","last_updated":"2025-06-03T03:02:47Z","snapshot_observed_at":"2026-08-09T09:03:10.834019Z","submitted_at":"2025-06-03T03:02:47Z","title":"Univariate to Multivariate: LLMs as Zero-Shot Predictors for Time-Series Forecasting","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:14.513983Z"},"links":{"cited_paper":"/paper/2504.00226","citing_paper":"/paper/2506.02389"},"observation_digest":"sha256:f14a9d42412b74d2c20389ffe30d286cd3d3bfa293323a3e3749f0618c45665a","observation_id":"5ce26483-1d62-431a-a6cc-bc28123290ae","resolution":{"observed_at":"2026-08-07T11:30:14.513983Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:30:15.415371Z","title":"Weatherbench: a benchmark data set for dat a-driven weather forecasting","venue":null,"work_id":"d9796845-be01-4836-b631-ed1c2c348609","year":2020},"citing_paper":{"arxiv_id":"2506.02389","last_updated":"2025-06-03T03:02:47Z","snapshot_observed_at":"2026-08-09T09:03:10.834019Z","submitted_at":"2025-06-03T03:02:47Z","title":"Univariate to Multivariate: LLMs as Zero-Shot Predictors for Time-Series Forecasting","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:14.704099Z"},"links":{"citing_paper":"/paper/2506.02389"},"observation_digest":"sha256:73257df646935991d29a084fbc6a3088cc7eeb63a5ac8f55c874e0f5420eed15","observation_id":"fa4ba504-c079-43dd-b016-3ecb43a4703a","resolution":{"observed_at":"2026-08-07T11:30:15.417664Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:30:15.409076Z","title":"Llm processes: Numerical predictive distributions condition ed on natural language","venue":null,"work_id":"ad5ff7d6-5981-4f08-ba4b-b381cca90ee1","year":2024},"citing_paper":{"arxiv_id":"2506.02389","last_updated":"2025-06-03T03:02:47Z","snapshot_observed_at":"2026-08-09T09:03:10.834019Z","submitted_at":"2025-06-03T03:02:47Z","title":"Univariate to Multivariate: LLMs as Zero-Shot Predictors for Time-Series Forecasting","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:14.802183Z"},"links":{"citing_paper":"/paper/2506.02389"},"observation_digest":"sha256:26a02671ff646840d0595c5e881fe6732620b37c1ad583aacd71f45365802a14","observation_id":"9a636586-5572-4016-9991-394ce589464b","resolution":{"observed_at":"2026-08-07T11:30:15.411441Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:30:15.402448Z","title":"Netdiffus : Network trafﬁc generation by diffusion models through time-series imaging","venue":null,"work_id":"2027f0da-6cbb-4e00-9233-4a014cf28d96","year":2024},"citing_paper":{"arxiv_id":"2506.02389","last_updated":"2025-06-03T03:02:47Z","snapshot_observed_at":"2026-08-09T09:03:10.834019Z","submitted_at":"2025-06-03T03:02:47Z","title":"Univariate to Multivariate: LLMs as Zero-Shot Predictors for Time-Series Forecasting","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:14.858825Z"},"links":{"citing_paper":"/paper/2506.02389"},"observation_digest":"sha256:6c3b0a237dbaeab129336a16dc5a9a6f6ca1b6687310882597a8dcf097b71814","observation_id":"ab1834bf-2240-426e-b9a6-a7bd3b07919e","resolution":{"observed_at":"2026-08-07T11:30:15.405001Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:30:15.396073Z","title":"A survey of transformer enabled time series synthesis","venue":null,"work_id":"c46a1e76-23da-4902-bcc6-62f3b9800a8f","year":2024},"citing_paper":{"arxiv_id":"2506.02389","last_updated":"2025-06-03T03:02:47Z","snapshot_observed_at":"2026-08-09T09:03:10.834019Z","submitted_at":"2025-06-03T03:02:47Z","title":"Univariate to Multivariate: LLMs as Zero-Shot Predictors for Time-Series Forecasting","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:14.923573Z"},"links":{"citing_paper":"/paper/2506.02389"},"observation_digest":"sha256:7f89b9a00239b1d7545ce73b86892ef0f8b53615deb9b63f8d503f90d99a261e","observation_id":"f2cb7e60-5392-4377-9ee9-7f14a4c89090","resolution":{"observed_at":"2026-08-07T11:30:15.398262Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2307.09288","last_updated":"2023-07-19T17:08:59Z","snapshot_observed_at":"2026-08-07T12:56:43.323460Z","submitted_at":"2023-07-18T14:31:57Z","title":"Llama 2: Open Foundation and Fine-Tuned Chat Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.09288","snapshot_observed_at":"2026-08-07T11:30:15.036355Z","title":"Llama 2: Open foundation and ﬁne-tuned chat models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.02389","last_updated":"2025-06-03T03:02:47Z","snapshot_observed_at":"2026-08-09T09:03:10.834019Z","submitted_at":"2025-06-03T03:02:47Z","title":"Univariate to Multivariate: LLMs as Zero-Shot Predictors for Time-Series Forecasting","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:15.036355Z"},"links":{"cited_paper":"/paper/2307.09288","citing_paper":"/paper/2506.02389"},"observation_digest":"sha256:9fd5decf064f00b5e361a6e09c111458a5f5ae0b690fc292b85d66febb5f541e","observation_id":"e3b28d00-8ef0-4695-808d-0d3dfe8c0488","resolution":{"observed_at":"2026-08-07T11:30:15.036355Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.07544","last_updated":"2024-09-10T20:35:25Z","snapshot_observed_at":"2026-08-07T09:31:24.456596Z","submitted_at":"2024-04-11T08:12:43Z","title":"From Words to Numbers: Your Large Language Model Is Secretly A Capable Regressor When Given In-Context Examples","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.07544","snapshot_observed_at":"2026-08-07T11:30:15.127917Z","title":"From words to numbers: Y our large language model is secretly a capable reg ressor when given in-context examples","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.02389","last_updated":"2025-06-03T03:02:47Z","snapshot_observed_at":"2026-08-09T09:03:10.834019Z","submitted_at":"2025-06-03T03:02:47Z","title":"Univariate to Multivariate: LLMs as Zero-Shot Predictors for Time-Series Forecasting","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:15.127917Z"},"links":{"cited_paper":"/paper/2404.07544","citing_paper":"/paper/2506.02389"},"observation_digest":"sha256:b65f47fafcb919bbcb65b89343bd1e89bc743f2f5c85bbd536166c97fbe3c4aa","observation_id":"8c4f52b1-5f99-40aa-aaf5-d56b088c7904","resolution":{"observed_at":"2026-08-07T11:30:15.127917Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:30:15.389941Z","title":"Learning latent seasonal-trend representations for time s eries forecasting","venue":null,"work_id":"7d1ecc1d-495f-4f32-a4e7-6e3314a4e344","year":2022},"citing_paper":{"arxiv_id":"2506.02389","last_updated":"2025-06-03T03:02:47Z","snapshot_observed_at":"2026-08-09T09:03:10.834019Z","submitted_at":"2025-06-03T03:02:47Z","title":"Univariate to Multivariate: LLMs as Zero-Shot Predictors for Time-Series Forecasting","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:15.149783Z"},"links":{"citing_paper":"/paper/2506.02389"},"observation_digest":"sha256:3400229e2d5c69aedb24587d1c4270fe6dfc77134fda6e20ed4879e051cddd58","observation_id":"579c98db-0217-48b0-98ad-408a842f382c","resolution":{"observed_at":"2026-08-07T11:30:15.392060Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:30:15.383172Z","title":"A utoformer: Decomposition transformers with auto-correlation for long-term series f orecasting","venue":null,"work_id":"3c75706a-1faf-4a5a-b8ca-b0dece83a21c","year":2021},"citing_paper":{"arxiv_id":"2506.02389","last_updated":"2025-06-03T03:02:47Z","snapshot_observed_at":"2026-08-09T09:03:10.834019Z","submitted_at":"2025-06-03T03:02:47Z","title":"Univariate to Multivariate: LLMs as Zero-Shot Predictors for Time-Series Forecasting","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:15.157109Z"},"links":{"citing_paper":"/paper/2506.02389"},"observation_digest":"sha256:d60025b2d0f5d8fc2fbddc2a53efc39fb08e404e1c02f59d5a1a922e5c94f227","observation_id":"8d0c3e50-47d0-46fa-89c8-0286ff4f0d79","resolution":{"observed_at":"2026-08-07T11:30:15.385644Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:30:15.375765Z","title":"Adversarial sparse transformer for time series forecasting","venue":null,"work_id":"f05932d3-3953-447d-99c8-01822d0c4cea","year":2020},"citing_paper":{"arxiv_id":"2506.02389","last_updated":"2025-06-03T03:02:47Z","snapshot_observed_at":"2026-08-09T09:03:10.834019Z","submitted_at":"2025-06-03T03:02:47Z","title":"Univariate to Multivariate: LLMs as Zero-Shot Predictors for Time-Series Forecasting","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:15.243848Z"},"links":{"citing_paper":"/paper/2506.02389"},"observation_digest":"sha256:d8a9a451d3ebed6a05672d8d02ecaeccef7b0e26779273e9a8ce3d0c1275eb1e","observation_id":"46c6448f-6980-48b5-9cce-847ee92df9f9","resolution":{"observed_at":"2026-08-07T11:30:15.378435Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:30:15.368601Z","title":"Promptcast: A new prompt-base d learning paradigm for time series forecasting","venue":null,"work_id":"3fb78807-b906-40ab-97b8-53c07e1f383e","year":2023},"citing_paper":{"arxiv_id":"2506.02389","last_updated":"2025-06-03T03:02:47Z","snapshot_observed_at":"2026-08-09T09:03:10.834019Z","submitted_at":"2025-06-03T03:02:47Z","title":"Univariate to Multivariate: LLMs as Zero-Shot Predictors for Time-Series Forecasting","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:15.246210Z"},"links":{"citing_paper":"/paper/2506.02389"},"observation_digest":"sha256:1ec1939e9da1c7b3a223c8874ccd8e0cd1187ae441111d1a378747b066b4b64c","observation_id":"6386c2ff-fc60-4a9c-a8f0-54ad80989866","resolution":{"observed_at":"2026-08-07T11:30:15.371286Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:30:15.361105Z","title":"Fouriergnn: Rethinking multivariate time se ries forecasting from a pure graph perspective","venue":null,"work_id":"0aca7061-e4d3-4f84-bf25-1b9bfa85f9f1","year":2023},"citing_paper":{"arxiv_id":"2506.02389","last_updated":"2025-06-03T03:02:47Z","snapshot_observed_at":"2026-08-09T09:03:10.834019Z","submitted_at":"2025-06-03T03:02:47Z","title":"Univariate to Multivariate: LLMs as Zero-Shot Predictors for Time-Series Forecasting","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:15.248666Z"},"links":{"citing_paper":"/paper/2506.02389"},"observation_digest":"sha256:0ed4d39426c996e0fdb17fbbc5e2a9d9cef733be0c8951d14f7a58d96b22ac40","observation_id":"25613631-35db-4f93-8bce-9a14e13fae19","resolution":{"observed_at":"2026-08-07T11:30:15.363588Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:30:15.354184Z","title":"Frequency-domain MLPs are m ore effective learners in time series forecasting","venue":null,"work_id":"27185617-a299-4203-a344-7572b48965e1","year":2023},"citing_paper":{"arxiv_id":"2506.02389","last_updated":"2025-06-03T03:02:47Z","snapshot_observed_at":"2026-08-09T09:03:10.834019Z","submitted_at":"2025-06-03T03:02:47Z","title":"Univariate to Multivariate: LLMs as Zero-Shot Predictors for Time-Series Forecasting","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:15.250773Z"},"links":{"citing_paper":"/paper/2506.02389"},"observation_digest":"sha256:4a7371f37a02abd647e1d4a22187503384681635852445c0202bfdaf0ea0d52a","observation_id":"c8562229-e391-4569-b79d-1db43e8cc5c8","resolution":{"observed_at":"2026-08-07T11:30:15.356466Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01000","last_updated":"2024-11-07T21:38:23Z","snapshot_observed_at":"2026-08-10T07:36:13.586462Z","submitted_at":"2024-02-01T20:27:19Z","title":"Multivariate Probabilistic Time Series Forecasting with Correlated Errors","version":4},"cited_work":{"arxiv_id":"2402.01000","doi":null,"metadata_source":"pith","pith_arxiv_id":"2402.01000","snapshot_observed_at":"2026-08-07T11:30:15.281405Z","title":"Multivariate Probabilistic Time Series Forecasting with Correlated Errors","venue":"stat.ML","work_id":"26d71554-4943-4c5c-9dce-04f4ee7902be","year":2024},"citing_paper":{"arxiv_id":"2506.02389","last_updated":"2025-06-03T03:02:47Z","snapshot_observed_at":"2026-08-09T09:03:10.834019Z","submitted_at":"2025-06-03T03:02:47Z","title":"Univariate to Multivariate: LLMs as Zero-Shot Predictors for Time-Series Forecasting","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:15.253117Z"},"links":{"cited_paper":"/paper/2402.01000","citing_paper":"/paper/2506.02389"},"observation_digest":"sha256:c8be4035ad6b0b0979b77109105b3fb0a376a482f26043207c933037f327db9e","observation_id":"225e818e-b400-4fdc-8ad1-79c58a9d286d","resolution":{"observed_at":"2026-08-07T11:30:15.285875Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:30:15.347402Z","title":"Informer: Beyond efﬁcient transformer for long s equence time-series forecasting","venue":null,"work_id":"6bbe800b-6f35-4a9e-91a7-96f58a62968a","year":2021},"citing_paper":{"arxiv_id":"2506.02389","last_updated":"2025-06-03T03:02:47Z","snapshot_observed_at":"2026-08-09T09:03:10.834019Z","submitted_at":"2025-06-03T03:02:47Z","title":"Univariate to Multivariate: LLMs as Zero-Shot Predictors for Time-Series Forecasting","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:15.256222Z"},"links":{"citing_paper":"/paper/2506.02389"},"observation_digest":"sha256:2a560625665c1861b82e45627a01d4a00229b03310d669a6151817c1f207ea4d","observation_id":"9786f002-d10d-4f32-bd4a-0ac3f22377cd","resolution":{"observed_at":"2026-08-07T11:30:15.350036Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:30:15.340751Z","title":"Fedformer: Frequency enhanced decomposed transformer for long-term s eries forecasting","venue":null,"work_id":"92798450-1e0a-4023-9b47-65afb64d2c27","year":2022},"citing_paper":{"arxiv_id":"2506.02389","last_updated":"2025-06-03T03:02:47Z","snapshot_observed_at":"2026-08-09T09:03:10.834019Z","submitted_at":"2025-06-03T03:02:47Z","title":"Univariate to Multivariate: LLMs as Zero-Shot Predictors for Time-Series Forecasting","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:15.258473Z"},"links":{"citing_paper":"/paper/2506.02389"},"observation_digest":"sha256:a9ed05339e656589d5b7db8c7d19ac41c490e164b146327bca517b7ac5392e6e","observation_id":"a5dfe175-05ec-41ce-8909-0d775eae2e7d","resolution":{"observed_at":"2026-08-07T11:30:15.343358Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:30:15.333671Z","title":"Consider the distribution. Predict the next few lines. INT EGER component of the value SHOULD be SAME as the train data. ONLY provide numerica l values","venue":null,"work_id":"4ad877e9-1ce9-4b22-95f1-b7f1e1d9a7f4","year":2025},"citing_paper":{"arxiv_id":"2506.02389","last_updated":"2025-06-03T03:02:47Z","snapshot_observed_at":"2026-08-09T09:03:10.834019Z","submitted_at":"2025-06-03T03:02:47Z","title":"Univariate to Multivariate: LLMs as Zero-Shot Predictors for Time-Series Forecasting","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:15.260931Z"},"links":{"citing_paper":"/paper/2506.02389"},"observation_digest":"sha256:2c054bd229c2e56dfd77083b83e30a9b7233ff5fc49ea9ecff088bf91168128a","observation_id":"5c7eb9b3-9cbd-4e53-ad28-a01eed6ab1ba","resolution":{"observed_at":"2026-08-07T11:30:15.336248Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:30:15.428049Z","title":null,"venue":null,"work_id":"4d29150a-00a9-4801-af83-ec309ee5dbfe","year":2025},"citing_paper":{"arxiv_id":"2506.02389","last_updated":"2025-06-03T03:02:47Z","snapshot_observed_at":"2026-08-09T09:03:10.834019Z","submitted_at":"2025-06-03T03:02:47Z","title":"Univariate to Multivariate: LLMs as Zero-Shot Predictors for Time-Series Forecasting","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-07T11:30:14.196513Z"},"links":{"citing_paper":"/paper/2506.02389"},"observation_digest":"sha256:093b6fb0a991f77483551bf39678ce9c013aa05193cf92d51b784b660bbf0348","observation_id":"3fdd9d8f-75ae-42b7-b1d8-98d384b8e354","resolution":{"observed_at":"2026-08-07T11:30:15.430377Z","resolver_source":"raw_fallback","status":"parse_uncertain"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2506.02389","last_updated":"2025-06-03T03:02:47Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-09T09:03:10.834019Z","submitted_at":"2025-06-03T03:02:47Z","title":"Univariate to Multivariate: LLMs as Zero-Shot Predictors for Time-Series Forecasting"},"reference_resolution":{"displayed":36,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":1,"unresolved":7,"verified_exact":1,"verified_fuzzy":26},"total_outbound_references":36},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2506.02389."}