{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:YK3TWWFKBH5HOF72KEM53UTS6U","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"37b8e5d7cafe4e756e3150f07ef97b83889b1da795f0e101e3780410372510f6","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-08-10T13:28:59Z","title_canon_sha256":"1bc4603561ea913b39ab721387431518f712fc85c11b500f6e0126accbaf74c9"},"schema_version":"1.0","source":{"id":"2308.05566","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2308.05566","created_at":"2026-07-05T06:40:02Z"},{"alias_kind":"arxiv_version","alias_value":"2308.05566v1","created_at":"2026-07-05T06:40:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.05566","created_at":"2026-07-05T06:40:02Z"},{"alias_kind":"pith_short_12","alias_value":"YK3TWWFKBH5H","created_at":"2026-07-05T06:40:02Z"},{"alias_kind":"pith_short_16","alias_value":"YK3TWWFKBH5HOF72","created_at":"2026-07-05T06:40:02Z"},{"alias_kind":"pith_short_8","alias_value":"YK3TWWFK","created_at":"2026-07-05T06:40:02Z"}],"graph_snapshots":[{"event_id":"sha256:20c45c116d2813d1eb584a0f67bff86b0cdbbbbe9751de0a3d932a619f406014","target":"graph","created_at":"2026-07-05T06:40:02Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2308.05566/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We introduce AutoGluon-TimeSeries - an open-source AutoML library for probabilistic time series forecasting. Focused on ease of use and robustness, AutoGluon-TimeSeries enables users to generate accurate point and quantile forecasts with just 3 lines of Python code. Built on the design philosophy of AutoGluon, AutoGluon-TimeSeries leverages ensembles of diverse forecasting models to deliver high accuracy within a short training time. AutoGluon-TimeSeries combines both conventional statistical models, machine-learning based forecasting approaches, and ensembling techniques. In our evaluation on","authors_text":"Alexander Shirkov, Caner Turkmen, Huibin Shen, Nick Erickson, Oleksandr Shchur, Tony Hu, Yuyang Wang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-08-10T13:28:59Z","title":"AutoGluon-TimeSeries: AutoML for Probabilistic Time Series Forecasting"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.05566","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:9a6fdbb53c5a667b9c4f6423d843253c01181b8c0e50b71e5b89f8f4b3fec144","target":"record","created_at":"2026-07-05T06:40:02Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"37b8e5d7cafe4e756e3150f07ef97b83889b1da795f0e101e3780410372510f6","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-08-10T13:28:59Z","title_canon_sha256":"1bc4603561ea913b39ab721387431518f712fc85c11b500f6e0126accbaf74c9"},"schema_version":"1.0","source":{"id":"2308.05566","kind":"arxiv","version":1}},"canonical_sha256":"c2b73b58aa09fa7717fa5119ddd272f53f9036cd9589a4ef0eafd9d60e724225","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c2b73b58aa09fa7717fa5119ddd272f53f9036cd9589a4ef0eafd9d60e724225","first_computed_at":"2026-07-05T06:40:02.440284Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:40:02.440284Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"FoV/LEXUaTSUOnvEbWCRZOr+vgpnICL2QlrLy3/x+dve0WCP4BV6VH2SVVmCMIva9/FVXQKUdpHYyhzE+BQjDg==","signature_status":"signed_v1","signed_at":"2026-07-05T06:40:02.440734Z","signed_message":"canonical_sha256_bytes"},"source_id":"2308.05566","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9a6fdbb53c5a667b9c4f6423d843253c01181b8c0e50b71e5b89f8f4b3fec144","sha256:20c45c116d2813d1eb584a0f67bff86b0cdbbbbe9751de0a3d932a619f406014"],"state_sha256":"a733bf1bb90310a98b6b39177c2214c66750eb278d3bb84f3b2ab03ba931bb79"}