{"as_of":"2026-08-05T07:38:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:9d8c81aac7ca0244e1aa55797ecc10bb5b28c09a03d2da6a65b0a9b6db19470a","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":4,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":4,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-05T06:32:48.257954+00:00","state":"measured"},{"denominator":4,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":4,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-03T05:10:05.608454Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-19T09:32:16.095866Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2402.16132","last_updated":"2024-02-25T16:14:26Z","snapshot_observed_at":"2026-07-06T17:35:10.248760Z","submitted_at":"2024-02-25T16:14:26Z","title":"LSTPrompt: Large Language Models as Zero-Shot Time Series Forecasters by Long-Short-Term Prompting","version":1},"cited_work":{"arxiv_id":"2402.16132","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.16132","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Lstprompt: Large language models as zero-shot time series forecasters by long-short-term prompting","venue":null,"work_id":"a0959756-22d8-4470-a2a1-0973ed3f486b","year":2024},"citing_paper":{"arxiv_id":"2506.10630","last_updated":"2026-06-03T16:47:53Z","snapshot_observed_at":"2026-07-06T21:41:00.113637Z","submitted_at":"2025-06-12T12:15:50Z","title":"Time Series Forecasting as Reasoning: A Slow-Thinking Approach with Reinforced LLMs","version":2},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-05-19T09:20:56.057422Z"},"links":{"cited_paper":"/paper/2402.16132","citing_paper":"/paper/2506.10630"},"observation_digest":"sha256:daafaab4f474d88d0e16eb3c1ffbd27c48fa180c4a1585d15119453fbaaa2b94","observation_id":"9ac31aea-8bc2-46ba-80e7-ba5a59551b7f","resolution":{"observed_at":"2026-05-19T09:22:14.252495Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.16132","last_updated":"2024-02-25T16:14:26Z","snapshot_observed_at":"2026-07-06T17:35:10.248760Z","submitted_at":"2024-02-25T16:14:26Z","title":"LSTPrompt: Large Language Models as Zero-Shot Time Series Forecasters by Long-Short-Term Prompting","version":1},"cited_work":{"arxiv_id":"2402.16132","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.16132","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Lstprompt: Large language models as zero-shot time series forecasters by long-short-term prompting","venue":null,"work_id":"a0959756-22d8-4470-a2a1-0973ed3f486b","year":2024},"citing_paper":{"arxiv_id":"2506.11512","last_updated":"2026-05-08T13:38:42Z","snapshot_observed_at":"2026-07-06T21:41:33.629441Z","submitted_at":"2025-06-13T07:13:05Z","title":"From Time Series Analysis to Question Answering: A Survey in the LLM Era","version":2},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-05-19T09:31:55.829045Z"},"links":{"cited_paper":"/paper/2402.16132","citing_paper":"/paper/2506.11512"},"observation_digest":"sha256:d089f513e10d76ec1b9d073ad9c035014584f3cbcefefb2b4dd3bc1d582d3628","observation_id":"3334e8d5-48a7-4d87-9f09-d8035958484f","resolution":{"observed_at":"2026-05-19T09:32:16.099947Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.16132","last_updated":"2024-02-25T16:14:26Z","snapshot_observed_at":"2026-07-06T17:35:10.248760Z","submitted_at":"2024-02-25T16:14:26Z","title":"LSTPrompt: Large Language Models as Zero-Shot Time Series Forecasters by Long-Short-Term Prompting","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.16132","snapshot_observed_at":"2026-08-03T05:10:05.608454Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2602.03164","last_updated":"2026-06-09T12:38:38Z","snapshot_observed_at":"2026-08-03T05:10:01.121039Z","submitted_at":"2026-02-03T06:31:40Z","title":"MemCast: Memory-Driven Time Series Forecasting with Experience-Conditioned Reasoning","version":2},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-03T05:10:05.608454Z"},"links":{"cited_paper":"/paper/2402.16132","citing_paper":"/paper/2602.03164"},"observation_digest":"sha256:fcc65f2c2dd3623ff9b440c737c5441ae0bfe62f81acf9c19e5269e7c9873479","observation_id":"323e567b-cae0-4722-ace0-dfd736d69ed0","resolution":{"observed_at":"2026-08-03T05:10:05.608454Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.16132","last_updated":"2024-02-25T16:14:26Z","snapshot_observed_at":"2026-07-06T17:35:10.248760Z","submitted_at":"2024-02-25T16:14:26Z","title":"LSTPrompt: Large Language Models as Zero-Shot Time Series Forecasters by Long-Short-Term Prompting","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.16132","snapshot_observed_at":"2026-08-03T05:00:54.853350Z","title":"Time-mmd: Multi-domain multimodal dataset for time series analysis.Advances in Neural Information Processing Systems, 37:77888–77933, 2024a","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2602.03564","last_updated":"2026-07-11T18:04:42Z","snapshot_observed_at":"2026-08-03T05:00:52.512018Z","submitted_at":"2026-02-03T14:08:10Z","title":"CoGenCast: A Coupled Autoregressive-Flow Generative Framework for Time Series Forecasting","version":2},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-03T05:00:54.853350Z"},"links":{"cited_paper":"/paper/2402.16132","citing_paper":"/paper/2602.03564"},"observation_digest":"sha256:b29ec8a6c74f8dbe0e6a710402691e85e5e51353a24495535a80fe8373e5e8b2","observation_id":"a76383cc-d41d-4f07-b159-95d329b140ae","resolution":{"observed_at":"2026-08-03T05:00:54.853350Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2402.16132/citation-record","integrity":"/paper/2402.16132/integrity","json":"/paper/2402.16132/citation-record.json","paper":"/paper/2402.16132"},"outbound":[],"paper":{"arxiv_id":"2402.16132","last_updated":"2024-02-25T16:14:26Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-07-06T17:35:10.248760Z","submitted_at":"2024-02-25T16:14:26Z","title":"LSTPrompt: Large Language Models as Zero-Shot Time Series Forecasters by Long-Short-Term Prompting"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"thesis":"As of 5 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2402.16132."}