{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:JCBGZXV2Y4RTLIQVXVGRAJROZA","short_pith_number":"pith:JCBGZXV2","canonical_record":{"source":{"id":"2102.12061","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-02-24T04:27:23Z","cross_cats_sorted":["cs.LG","econ.EM"],"title_canon_sha256":"f9929bcfc8ee195a82bba7de0d897278c73593e1ebc8227486f1ef4e0dc0d5b4","abstract_canon_sha256":"07b387d73cd4f84b6aa29d38fd41d0f9fd43707995e2cf4b19f70cf989d83b00"},"schema_version":"1.0"},"canonical_sha256":"48826cdebac72335a215bd4d10262ec8125f9fd9bce61055008e6b3cbc024601","source":{"kind":"arxiv","id":"2102.12061","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2102.12061","created_at":"2026-07-05T03:28:50Z"},{"alias_kind":"arxiv_version","alias_value":"2102.12061v2","created_at":"2026-07-05T03:28:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2102.12061","created_at":"2026-07-05T03:28:50Z"},{"alias_kind":"pith_short_12","alias_value":"JCBGZXV2Y4RT","created_at":"2026-07-05T03:28:50Z"},{"alias_kind":"pith_short_16","alias_value":"JCBGZXV2Y4RTLIQV","created_at":"2026-07-05T03:28:50Z"},{"alias_kind":"pith_short_8","alias_value":"JCBGZXV2","created_at":"2026-07-05T03:28:50Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:JCBGZXV2Y4RTLIQVXVGRAJROZA","target":"record","payload":{"canonical_record":{"source":{"id":"2102.12061","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-02-24T04:27:23Z","cross_cats_sorted":["cs.LG","econ.EM"],"title_canon_sha256":"f9929bcfc8ee195a82bba7de0d897278c73593e1ebc8227486f1ef4e0dc0d5b4","abstract_canon_sha256":"07b387d73cd4f84b6aa29d38fd41d0f9fd43707995e2cf4b19f70cf989d83b00"},"schema_version":"1.0"},"canonical_sha256":"48826cdebac72335a215bd4d10262ec8125f9fd9bce61055008e6b3cbc024601","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:28:50.745496Z","signature_b64":"mBl+zhDtghg4KEMqw04SJC/z9L/g3RNvwSsZ1sZSChIwW4VHOArqmxZDeTiODfCHrdUZ1DzrW65JF7bDCyMsBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"48826cdebac72335a215bd4d10262ec8125f9fd9bce61055008e6b3cbc024601","last_reissued_at":"2026-07-05T03:28:50.745006Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:28:50.745006Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2102.12061","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T03:28:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pJOWgB0+d3y5vzAo6LZSM38J+zZ94BDRAJvZ/OIQeidkZKyYJbAMNflPSUmXCdyw59ub1t2M1yxY4J5ZC4CtBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T15:41:30.633898Z"},"content_sha256":"1a96ba034d9b8b6de879127f578fd7abf6846a23e4882df64ac777931a1faf4a","schema_version":"1.0","event_id":"sha256:1a96ba034d9b8b6de879127f578fd7abf6846a23e4882df64ac777931a1faf4a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:JCBGZXV2Y4RTLIQVXVGRAJROZA","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Deep Video Prediction for Time Series Forecasting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","econ.EM"],"primary_cat":"cs.CV","authors_text":"Manuela Veloso, Tucker Balch, Zhen Zeng","submitted_at":"2021-02-24T04:27:23Z","abstract_excerpt":"Time series forecasting is essential for decision making in many domains. In this work, we address the challenge of predicting prices evolution among multiple potentially interacting financial assets. A solution to this problem has obvious importance for governments, banks, and investors. Statistical methods such as Auto Regressive Integrated Moving Average (ARIMA) are widely applied to these problems. In this paper, we propose to approach economic time series forecasting of multiple financial assets in a novel way via video prediction. Given past prices of multiple potentially interacting fin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2102.12061","kind":"arxiv","version":2},"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/2102.12061/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":"2026-07-05T03:28:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SoDXsi1gnTJLFwTTOJpGV0OlTRIetZ3QMMSqEWl/61jKL5i3uANa7VCWt6jAIno96aCjHq0gDXx/U84CU338Cg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T15:41:30.634288Z"},"content_sha256":"a6d56e9e9dfb953d1b546545cc0d291226c1a6d1c3c3f33b73412f83c44492eb","schema_version":"1.0","event_id":"sha256:a6d56e9e9dfb953d1b546545cc0d291226c1a6d1c3c3f33b73412f83c44492eb"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/JCBGZXV2Y4RTLIQVXVGRAJROZA/bundle.json","state_url":"https://pith.science/pith/JCBGZXV2Y4RTLIQVXVGRAJROZA/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/JCBGZXV2Y4RTLIQVXVGRAJROZA/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":"2026-07-06T15:41:30Z","links":{"resolver":"https://pith.science/pith/JCBGZXV2Y4RTLIQVXVGRAJROZA","bundle":"https://pith.science/pith/JCBGZXV2Y4RTLIQVXVGRAJROZA/bundle.json","state":"https://pith.science/pith/JCBGZXV2Y4RTLIQVXVGRAJROZA/state.json","well_known_bundle":"https://pith.science/.well-known/pith/JCBGZXV2Y4RTLIQVXVGRAJROZA/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:JCBGZXV2Y4RTLIQVXVGRAJROZA","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":"07b387d73cd4f84b6aa29d38fd41d0f9fd43707995e2cf4b19f70cf989d83b00","cross_cats_sorted":["cs.LG","econ.EM"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-02-24T04:27:23Z","title_canon_sha256":"f9929bcfc8ee195a82bba7de0d897278c73593e1ebc8227486f1ef4e0dc0d5b4"},"schema_version":"1.0","source":{"id":"2102.12061","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2102.12061","created_at":"2026-07-05T03:28:50Z"},{"alias_kind":"arxiv_version","alias_value":"2102.12061v2","created_at":"2026-07-05T03:28:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2102.12061","created_at":"2026-07-05T03:28:50Z"},{"alias_kind":"pith_short_12","alias_value":"JCBGZXV2Y4RT","created_at":"2026-07-05T03:28:50Z"},{"alias_kind":"pith_short_16","alias_value":"JCBGZXV2Y4RTLIQV","created_at":"2026-07-05T03:28:50Z"},{"alias_kind":"pith_short_8","alias_value":"JCBGZXV2","created_at":"2026-07-05T03:28:50Z"}],"graph_snapshots":[{"event_id":"sha256:a6d56e9e9dfb953d1b546545cc0d291226c1a6d1c3c3f33b73412f83c44492eb","target":"graph","created_at":"2026-07-05T03:28:50Z","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/2102.12061/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Time series forecasting is essential for decision making in many domains. In this work, we address the challenge of predicting prices evolution among multiple potentially interacting financial assets. A solution to this problem has obvious importance for governments, banks, and investors. Statistical methods such as Auto Regressive Integrated Moving Average (ARIMA) are widely applied to these problems. In this paper, we propose to approach economic time series forecasting of multiple financial assets in a novel way via video prediction. Given past prices of multiple potentially interacting fin","authors_text":"Manuela Veloso, Tucker Balch, Zhen Zeng","cross_cats":["cs.LG","econ.EM"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-02-24T04:27:23Z","title":"Deep Video Prediction for Time Series Forecasting"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2102.12061","kind":"arxiv","version":2},"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":"sha256:1a96ba034d9b8b6de879127f578fd7abf6846a23e4882df64ac777931a1faf4a","target":"record","created_at":"2026-07-05T03:28:50Z","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":"07b387d73cd4f84b6aa29d38fd41d0f9fd43707995e2cf4b19f70cf989d83b00","cross_cats_sorted":["cs.LG","econ.EM"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-02-24T04:27:23Z","title_canon_sha256":"f9929bcfc8ee195a82bba7de0d897278c73593e1ebc8227486f1ef4e0dc0d5b4"},"schema_version":"1.0","source":{"id":"2102.12061","kind":"arxiv","version":2}},"canonical_sha256":"48826cdebac72335a215bd4d10262ec8125f9fd9bce61055008e6b3cbc024601","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"48826cdebac72335a215bd4d10262ec8125f9fd9bce61055008e6b3cbc024601","first_computed_at":"2026-07-05T03:28:50.745006Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:28:50.745006Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"mBl+zhDtghg4KEMqw04SJC/z9L/g3RNvwSsZ1sZSChIwW4VHOArqmxZDeTiODfCHrdUZ1DzrW65JF7bDCyMsBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T03:28:50.745496Z","signed_message":"canonical_sha256_bytes"},"source_id":"2102.12061","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1a96ba034d9b8b6de879127f578fd7abf6846a23e4882df64ac777931a1faf4a","sha256:a6d56e9e9dfb953d1b546545cc0d291226c1a6d1c3c3f33b73412f83c44492eb"],"state_sha256":"bb15dcc2428dbca815b5f15d0b9ac59441c78e1b0198b0d5eba2efa9cf2f56ae"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bBX6kV/7FcRsXgC9ponaqpwLdCz1jJ4ISBbDE2AV0AdKz2Em4RvXnQGVk0ATUgCVGLUbiIkxWe78OtVhToiKDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-06T15:41:30.636295Z","bundle_sha256":"42c8d6cb22072f404ba493ea03d6cc2b915ee939bc6848ae0c3494598a98fa2b"}}