{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:HAEDLWHFFFNYPYABQGLDXNMZLQ","short_pith_number":"pith:HAEDLWHF","canonical_record":{"source":{"id":"2303.07048","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-03-13T12:13:28Z","cross_cats_sorted":[],"title_canon_sha256":"17e48466708096fead96f027aff97bb828dd2e2d43c744ebd566e49e86a993ba","abstract_canon_sha256":"7c52b2962b6d86d5e6f455d36f26214b4851b58bf74e76e509529f3db05a744a"},"schema_version":"1.0"},"canonical_sha256":"380835d8e5295b87e00181963bb5995c0de2eab8a7d5fdf8366bb9d07ce5d137","source":{"kind":"arxiv","id":"2303.07048","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2303.07048","created_at":"2026-07-05T07:11:54Z"},{"alias_kind":"arxiv_version","alias_value":"2303.07048v1","created_at":"2026-07-05T07:11:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.07048","created_at":"2026-07-05T07:11:54Z"},{"alias_kind":"pith_short_12","alias_value":"HAEDLWHFFFNY","created_at":"2026-07-05T07:11:54Z"},{"alias_kind":"pith_short_16","alias_value":"HAEDLWHFFFNYPYAB","created_at":"2026-07-05T07:11:54Z"},{"alias_kind":"pith_short_8","alias_value":"HAEDLWHF","created_at":"2026-07-05T07:11:54Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:HAEDLWHFFFNYPYABQGLDXNMZLQ","target":"record","payload":{"canonical_record":{"source":{"id":"2303.07048","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-03-13T12:13:28Z","cross_cats_sorted":[],"title_canon_sha256":"17e48466708096fead96f027aff97bb828dd2e2d43c744ebd566e49e86a993ba","abstract_canon_sha256":"7c52b2962b6d86d5e6f455d36f26214b4851b58bf74e76e509529f3db05a744a"},"schema_version":"1.0"},"canonical_sha256":"380835d8e5295b87e00181963bb5995c0de2eab8a7d5fdf8366bb9d07ce5d137","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:11:54.558523Z","signature_b64":"z5DiFatUe+CZykUsNXQaEbuHD9jIzV/vJgdu43kaoOwZZq6FGl2lrgvTgrGHbkGvafvhL58UxYJulIjRKwJQDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"380835d8e5295b87e00181963bb5995c0de2eab8a7d5fdf8366bb9d07ce5d137","last_reissued_at":"2026-07-05T07:11:54.558039Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:11:54.558039Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2303.07048","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:11:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"uQhWbRcDjgSww76ZSVnBoVaQ1GxARKT2sA2AyrnW6V+PX3Rr8YIovbs6+NCJ8Vu9uxEqVI+f9l2rKitZC/6iCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T04:09:20.776143Z"},"content_sha256":"3765f8741d91521a4fe7b8d4cdccc32cabe8fdefc4d7686a677eb41da88aa499","schema_version":"1.0","event_id":"sha256:3765f8741d91521a4fe7b8d4cdccc32cabe8fdefc4d7686a677eb41da88aa499"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:HAEDLWHFFFNYPYABQGLDXNMZLQ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Hybrid Variational Autoencoder for Time Series Forecasting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Borui Cai, Longxiang Gao, Shuiqiao Yang, Yong Xiang","submitted_at":"2023-03-13T12:13:28Z","abstract_excerpt":"Variational autoencoders (VAE) are powerful generative models that learn the latent representations of input data as random variables. Recent studies show that VAE can flexibly learn the complex temporal dynamics of time series and achieve more promising forecasting results than deterministic models. However, a major limitation of existing works is that they fail to jointly learn the local patterns (e.g., seasonality and trend) and temporal dynamics of time series for forecasting. Accordingly, we propose a novel hybrid variational autoencoder (HyVAE) to integrate the learning of local patterns"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.07048","kind":"arxiv","version":1},"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/2303.07048/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":"2026-07-05T07:11:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QZRTOlM3/MNAb1XH1H0zyqqidxJMRjKqZxpUdVZrbmVsPCwqr0XOf5r4zP/1NbXtx0WieQ1gPH9Qf8Z1QGLBCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T04:09:20.776529Z"},"content_sha256":"4343bd77dd8a2223b643574e33850bcae4a22b0296bee973caea75e3fd569c7d","schema_version":"1.0","event_id":"sha256:4343bd77dd8a2223b643574e33850bcae4a22b0296bee973caea75e3fd569c7d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/HAEDLWHFFFNYPYABQGLDXNMZLQ/bundle.json","state_url":"https://pith.science/pith/HAEDLWHFFFNYPYABQGLDXNMZLQ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/HAEDLWHFFFNYPYABQGLDXNMZLQ/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":"2026-07-07T04:09:20Z","links":{"resolver":"https://pith.science/pith/HAEDLWHFFFNYPYABQGLDXNMZLQ","bundle":"https://pith.science/pith/HAEDLWHFFFNYPYABQGLDXNMZLQ/bundle.json","state":"https://pith.science/pith/HAEDLWHFFFNYPYABQGLDXNMZLQ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/HAEDLWHFFFNYPYABQGLDXNMZLQ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:HAEDLWHFFFNYPYABQGLDXNMZLQ","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":"7c52b2962b6d86d5e6f455d36f26214b4851b58bf74e76e509529f3db05a744a","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-03-13T12:13:28Z","title_canon_sha256":"17e48466708096fead96f027aff97bb828dd2e2d43c744ebd566e49e86a993ba"},"schema_version":"1.0","source":{"id":"2303.07048","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2303.07048","created_at":"2026-07-05T07:11:54Z"},{"alias_kind":"arxiv_version","alias_value":"2303.07048v1","created_at":"2026-07-05T07:11:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.07048","created_at":"2026-07-05T07:11:54Z"},{"alias_kind":"pith_short_12","alias_value":"HAEDLWHFFFNY","created_at":"2026-07-05T07:11:54Z"},{"alias_kind":"pith_short_16","alias_value":"HAEDLWHFFFNYPYAB","created_at":"2026-07-05T07:11:54Z"},{"alias_kind":"pith_short_8","alias_value":"HAEDLWHF","created_at":"2026-07-05T07:11:54Z"}],"graph_snapshots":[{"event_id":"sha256:4343bd77dd8a2223b643574e33850bcae4a22b0296bee973caea75e3fd569c7d","target":"graph","created_at":"2026-07-05T07:11:54Z","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/2303.07048/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Variational autoencoders (VAE) are powerful generative models that learn the latent representations of input data as random variables. Recent studies show that VAE can flexibly learn the complex temporal dynamics of time series and achieve more promising forecasting results than deterministic models. However, a major limitation of existing works is that they fail to jointly learn the local patterns (e.g., seasonality and trend) and temporal dynamics of time series for forecasting. Accordingly, we propose a novel hybrid variational autoencoder (HyVAE) to integrate the learning of local patterns","authors_text":"Borui Cai, Longxiang Gao, Shuiqiao Yang, Yong Xiang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-03-13T12:13:28Z","title":"Hybrid Variational Autoencoder for Time Series Forecasting"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.07048","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":"sha256:3765f8741d91521a4fe7b8d4cdccc32cabe8fdefc4d7686a677eb41da88aa499","target":"record","created_at":"2026-07-05T07:11:54Z","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":"7c52b2962b6d86d5e6f455d36f26214b4851b58bf74e76e509529f3db05a744a","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-03-13T12:13:28Z","title_canon_sha256":"17e48466708096fead96f027aff97bb828dd2e2d43c744ebd566e49e86a993ba"},"schema_version":"1.0","source":{"id":"2303.07048","kind":"arxiv","version":1}},"canonical_sha256":"380835d8e5295b87e00181963bb5995c0de2eab8a7d5fdf8366bb9d07ce5d137","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"380835d8e5295b87e00181963bb5995c0de2eab8a7d5fdf8366bb9d07ce5d137","first_computed_at":"2026-07-05T07:11:54.558039Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:11:54.558039Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"z5DiFatUe+CZykUsNXQaEbuHD9jIzV/vJgdu43kaoOwZZq6FGl2lrgvTgrGHbkGvafvhL58UxYJulIjRKwJQDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T07:11:54.558523Z","signed_message":"canonical_sha256_bytes"},"source_id":"2303.07048","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3765f8741d91521a4fe7b8d4cdccc32cabe8fdefc4d7686a677eb41da88aa499","sha256:4343bd77dd8a2223b643574e33850bcae4a22b0296bee973caea75e3fd569c7d"],"state_sha256":"215f4bafa69ff0435919f44ad80629882964da8dd066663c66f57f30fa5a97ed"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HmNpExgHBfrJvW5RirTli9rzEj21rHjH/12j5n/185bL1sq9Ae19YZcGXefdVqM6VTBw8n2R8ffcckHnZ+UWAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T04:09:20.778783Z","bundle_sha256":"6d06da07b0a6a65fc6515c3da65daf43a8a04461e3f38827f9402bedd18a1e21"}}