{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:6QTKPOAJPHQ2B3INNZ5OUY4D7T","short_pith_number":"pith:6QTKPOAJ","canonical_record":{"source":{"id":"2109.07438","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-09-15T17:13:47Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"5b695b08251bb43ed3786c44e15f71c5be29b5ea81958f87bc5abd792b56192b","abstract_canon_sha256":"d3d8a1f999e34b2a50ca08d734cb9bc38bc6515e19e88df0ef27ebb866c26291"},"schema_version":"1.0"},"canonical_sha256":"f426a7b80979e1a0ed0d6e7aea6383fcca81b62be813169c0e28207ef669eb10","source":{"kind":"arxiv","id":"2109.07438","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2109.07438","created_at":"2026-07-05T04:00:03Z"},{"alias_kind":"arxiv_version","alias_value":"2109.07438v3","created_at":"2026-07-05T04:00:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.07438","created_at":"2026-07-05T04:00:03Z"},{"alias_kind":"pith_short_12","alias_value":"6QTKPOAJPHQ2","created_at":"2026-07-05T04:00:03Z"},{"alias_kind":"pith_short_16","alias_value":"6QTKPOAJPHQ2B3IN","created_at":"2026-07-05T04:00:03Z"},{"alias_kind":"pith_short_8","alias_value":"6QTKPOAJ","created_at":"2026-07-05T04:00:03Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:6QTKPOAJPHQ2B3INNZ5OUY4D7T","target":"record","payload":{"canonical_record":{"source":{"id":"2109.07438","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-09-15T17:13:47Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"5b695b08251bb43ed3786c44e15f71c5be29b5ea81958f87bc5abd792b56192b","abstract_canon_sha256":"d3d8a1f999e34b2a50ca08d734cb9bc38bc6515e19e88df0ef27ebb866c26291"},"schema_version":"1.0"},"canonical_sha256":"f426a7b80979e1a0ed0d6e7aea6383fcca81b62be813169c0e28207ef669eb10","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:00:03.215389Z","signature_b64":"aX9sIPj18Xc3a7GZJHFUadMBjQmx9QHyN4rm5u97rRDRUk9qCHb4Z9lknh0pKs3L8rJ2StqMWLOhNhWUNOD3Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f426a7b80979e1a0ed0d6e7aea6383fcca81b62be813169c0e28207ef669eb10","last_reissued_at":"2026-07-05T04:00:03.214929Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:00:03.214929Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2109.07438","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:00:03Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"C4GY7KX8iDa6amI4nrf9Eq7qftfxsFTbn4thGOzbHBquHJlKXI6ovPdGMZyZTo6kl0L9P3uBFmbwNzl+QdwmAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-19T08:58:57.566410Z"},"content_sha256":"63df2b862e41cd009277718bc7816b025943f1efe499ec2878cd78d321b631a8","schema_version":"1.0","event_id":"sha256:63df2b862e41cd009277718bc7816b025943f1efe499ec2878cd78d321b631a8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:6QTKPOAJPHQ2B3INNZ5OUY4D7T","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"CAMul: Calibrated and Accurate Multi-view Time-Series Forecasting","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Alexander Rodr\\'iguez, B. Aditya Prakash, Chao Zhang, Harshavardhan Kamarthi, Lingkai Kong","submitted_at":"2021-09-15T17:13:47Z","abstract_excerpt":"Probabilistic time-series forecasting enables reliable decision making across many domains. Most forecasting problems have diverse sources of data containing multiple modalities and structures. Leveraging information as well as uncertainty from these data sources for well-calibrated and accurate forecasts is an important challenging problem. Most previous work on multi-modal learning and forecasting simply aggregate intermediate representations from each data view by simple methods of summation or concatenation and do not explicitly model uncertainty for each data-view. We propose a general pr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2109.07438","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2109.07438/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:00:03Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1x93bemym/v0ce2xZ2UmelwuSNe09E879CSQmXzxNS3n11MdrgQ/pVbF2d260ES1aHw7jP4qCmUtLRQhmQWGAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-19T08:58:57.567837Z"},"content_sha256":"0ede68427043394b783d76482c7875645bc55404ee5379e4d605d72cbc8ccd68","schema_version":"1.0","event_id":"sha256:0ede68427043394b783d76482c7875645bc55404ee5379e4d605d72cbc8ccd68"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/6QTKPOAJPHQ2B3INNZ5OUY4D7T/bundle.json","state_url":"https://pith.science/pith/6QTKPOAJPHQ2B3INNZ5OUY4D7T/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/6QTKPOAJPHQ2B3INNZ5OUY4D7T/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-19T08:58:57Z","links":{"resolver":"https://pith.science/pith/6QTKPOAJPHQ2B3INNZ5OUY4D7T","bundle":"https://pith.science/pith/6QTKPOAJPHQ2B3INNZ5OUY4D7T/bundle.json","state":"https://pith.science/pith/6QTKPOAJPHQ2B3INNZ5OUY4D7T/state.json","well_known_bundle":"https://pith.science/.well-known/pith/6QTKPOAJPHQ2B3INNZ5OUY4D7T/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:6QTKPOAJPHQ2B3INNZ5OUY4D7T","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":"d3d8a1f999e34b2a50ca08d734cb9bc38bc6515e19e88df0ef27ebb866c26291","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-09-15T17:13:47Z","title_canon_sha256":"5b695b08251bb43ed3786c44e15f71c5be29b5ea81958f87bc5abd792b56192b"},"schema_version":"1.0","source":{"id":"2109.07438","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2109.07438","created_at":"2026-07-05T04:00:03Z"},{"alias_kind":"arxiv_version","alias_value":"2109.07438v3","created_at":"2026-07-05T04:00:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.07438","created_at":"2026-07-05T04:00:03Z"},{"alias_kind":"pith_short_12","alias_value":"6QTKPOAJPHQ2","created_at":"2026-07-05T04:00:03Z"},{"alias_kind":"pith_short_16","alias_value":"6QTKPOAJPHQ2B3IN","created_at":"2026-07-05T04:00:03Z"},{"alias_kind":"pith_short_8","alias_value":"6QTKPOAJ","created_at":"2026-07-05T04:00:03Z"}],"graph_snapshots":[{"event_id":"sha256:0ede68427043394b783d76482c7875645bc55404ee5379e4d605d72cbc8ccd68","target":"graph","created_at":"2026-07-05T04:00:03Z","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/2109.07438/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Probabilistic time-series forecasting enables reliable decision making across many domains. Most forecasting problems have diverse sources of data containing multiple modalities and structures. Leveraging information as well as uncertainty from these data sources for well-calibrated and accurate forecasts is an important challenging problem. Most previous work on multi-modal learning and forecasting simply aggregate intermediate representations from each data view by simple methods of summation or concatenation and do not explicitly model uncertainty for each data-view. We propose a general pr","authors_text":"Alexander Rodr\\'iguez, B. Aditya Prakash, Chao Zhang, Harshavardhan Kamarthi, Lingkai Kong","cross_cats":["cs.AI","stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-09-15T17:13:47Z","title":"CAMul: Calibrated and Accurate Multi-view Time-Series Forecasting"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2109.07438","kind":"arxiv","version":3},"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":"sha256:63df2b862e41cd009277718bc7816b025943f1efe499ec2878cd78d321b631a8","target":"record","created_at":"2026-07-05T04:00:03Z","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":"d3d8a1f999e34b2a50ca08d734cb9bc38bc6515e19e88df0ef27ebb866c26291","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-09-15T17:13:47Z","title_canon_sha256":"5b695b08251bb43ed3786c44e15f71c5be29b5ea81958f87bc5abd792b56192b"},"schema_version":"1.0","source":{"id":"2109.07438","kind":"arxiv","version":3}},"canonical_sha256":"f426a7b80979e1a0ed0d6e7aea6383fcca81b62be813169c0e28207ef669eb10","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f426a7b80979e1a0ed0d6e7aea6383fcca81b62be813169c0e28207ef669eb10","first_computed_at":"2026-07-05T04:00:03.214929Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:00:03.214929Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"aX9sIPj18Xc3a7GZJHFUadMBjQmx9QHyN4rm5u97rRDRUk9qCHb4Z9lknh0pKs3L8rJ2StqMWLOhNhWUNOD3Dg==","signature_status":"signed_v1","signed_at":"2026-07-05T04:00:03.215389Z","signed_message":"canonical_sha256_bytes"},"source_id":"2109.07438","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:63df2b862e41cd009277718bc7816b025943f1efe499ec2878cd78d321b631a8","sha256:0ede68427043394b783d76482c7875645bc55404ee5379e4d605d72cbc8ccd68"],"state_sha256":"e955c9c4f967ebc5af2cabde79b822b2717ef967d34ea366eaee335716584fa8"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"KgiWkQLN4gRWWsKa8ry+uqAxNNECdpmJ4vfockw2HoyvLdV9wxVMvApdmYGHu7m7Nwj9PIeqoGFHiunEHNp6CQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-19T08:58:57.573333Z","bundle_sha256":"82c32c2c23bb97b0c70dd4664697f6245da69d88b856e26fc76ba54ffbd9c1aa"}}