{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:WLXZNVVUDL33LR6KJBMN2A3MU3","short_pith_number":"pith:WLXZNVVU","canonical_record":{"source":{"id":"2307.00066","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-06-30T18:12:22Z","cross_cats_sorted":[],"title_canon_sha256":"d39e3336f6791c7d64a416c24cd7950d82ebf1dfc7881cfe91d693601ab75b20","abstract_canon_sha256":"4a2a8fec922d0bed7d234a3c56d42be0c3b3f77709738f7092cce3840f896de5"},"schema_version":"1.0"},"canonical_sha256":"b2ef96d6b41af7b5c7ca4858dd036ca6ebbcc8a8154dac5555a7f8f58f07578f","source":{"kind":"arxiv","id":"2307.00066","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2307.00066","created_at":"2026-07-05T06:26:35Z"},{"alias_kind":"arxiv_version","alias_value":"2307.00066v1","created_at":"2026-07-05T06:26:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.00066","created_at":"2026-07-05T06:26:35Z"},{"alias_kind":"pith_short_12","alias_value":"WLXZNVVUDL33","created_at":"2026-07-05T06:26:35Z"},{"alias_kind":"pith_short_16","alias_value":"WLXZNVVUDL33LR6K","created_at":"2026-07-05T06:26:35Z"},{"alias_kind":"pith_short_8","alias_value":"WLXZNVVU","created_at":"2026-07-05T06:26:35Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:WLXZNVVUDL33LR6KJBMN2A3MU3","target":"record","payload":{"canonical_record":{"source":{"id":"2307.00066","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-06-30T18:12:22Z","cross_cats_sorted":[],"title_canon_sha256":"d39e3336f6791c7d64a416c24cd7950d82ebf1dfc7881cfe91d693601ab75b20","abstract_canon_sha256":"4a2a8fec922d0bed7d234a3c56d42be0c3b3f77709738f7092cce3840f896de5"},"schema_version":"1.0"},"canonical_sha256":"b2ef96d6b41af7b5c7ca4858dd036ca6ebbcc8a8154dac5555a7f8f58f07578f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:26:35.010806Z","signature_b64":"ATNFRlvS3AXJJMmrFQO/KnEKqIjaBOz3VcLBpkn8ZUJ8njyo5LRzYsRfnKKdg1ChXUV9En2KlQTHJtYXzI8HAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b2ef96d6b41af7b5c7ca4858dd036ca6ebbcc8a8154dac5555a7f8f58f07578f","last_reissued_at":"2026-07-05T06:26:35.010440Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:26:35.010440Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2307.00066","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":"2026-07-05T06:26:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"o6Yl87ZE8ZFyOufvp0JpmmZrN0vUthvtqlix6ERzBZZN27RnC+mne29zbunkTYRSyHlmYTwKcWly/Z16utz4Bg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T11:15:57.216884Z"},"content_sha256":"29b978ae5b8a8a633d4bace74b3bdeea790672c163aedfcff57a060fdf502783","schema_version":"1.0","event_id":"sha256:29b978ae5b8a8a633d4bace74b3bdeea790672c163aedfcff57a060fdf502783"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:WLXZNVVUDL33LR6KJBMN2A3MU3","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Improving the Transferability of Time Series Forecasting with Decomposition Adaptation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Qiang Wang, Yan Gao, Yan Wang","submitted_at":"2023-06-30T18:12:22Z","abstract_excerpt":"Due to effective pattern mining and feature representation, neural forecasting models based on deep learning have achieved great progress. The premise of effective learning is to collect sufficient data. However, in time series forecasting, it is difficult to obtain enough data, which limits the performance of neural forecasting models. To alleviate the data scarcity limitation, we design Sequence Decomposition Adaptation Network (SeDAN) which is a novel transfer architecture to improve forecasting performance on the target domain by aligning transferable knowledge from cross-domain datasets. "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.00066","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/2307.00066/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":"2026-07-05T06:26:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4/iRFKkIxL8XvCPfMQ3ObSXN0qORDpaJ8qFOOooPgAWDDODUOFpkC3RWj79q/9O5YRZiEnZvFpmXtzKWWXxjAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T11:15:57.217419Z"},"content_sha256":"5cb0a3a36fd684d35f4c3d423bf7037bac857c70f461893e2c497510a90c0df4","schema_version":"1.0","event_id":"sha256:5cb0a3a36fd684d35f4c3d423bf7037bac857c70f461893e2c497510a90c0df4"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/WLXZNVVUDL33LR6KJBMN2A3MU3/bundle.json","state_url":"https://pith.science/pith/WLXZNVVUDL33LR6KJBMN2A3MU3/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/WLXZNVVUDL33LR6KJBMN2A3MU3/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":"2026-08-07T11:15:57Z","links":{"resolver":"https://pith.science/pith/WLXZNVVUDL33LR6KJBMN2A3MU3","bundle":"https://pith.science/pith/WLXZNVVUDL33LR6KJBMN2A3MU3/bundle.json","state":"https://pith.science/pith/WLXZNVVUDL33LR6KJBMN2A3MU3/state.json","well_known_bundle":"https://pith.science/.well-known/pith/WLXZNVVUDL33LR6KJBMN2A3MU3/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:WLXZNVVUDL33LR6KJBMN2A3MU3","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":"4a2a8fec922d0bed7d234a3c56d42be0c3b3f77709738f7092cce3840f896de5","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-06-30T18:12:22Z","title_canon_sha256":"d39e3336f6791c7d64a416c24cd7950d82ebf1dfc7881cfe91d693601ab75b20"},"schema_version":"1.0","source":{"id":"2307.00066","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2307.00066","created_at":"2026-07-05T06:26:35Z"},{"alias_kind":"arxiv_version","alias_value":"2307.00066v1","created_at":"2026-07-05T06:26:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.00066","created_at":"2026-07-05T06:26:35Z"},{"alias_kind":"pith_short_12","alias_value":"WLXZNVVUDL33","created_at":"2026-07-05T06:26:35Z"},{"alias_kind":"pith_short_16","alias_value":"WLXZNVVUDL33LR6K","created_at":"2026-07-05T06:26:35Z"},{"alias_kind":"pith_short_8","alias_value":"WLXZNVVU","created_at":"2026-07-05T06:26:35Z"}],"graph_snapshots":[{"event_id":"sha256:5cb0a3a36fd684d35f4c3d423bf7037bac857c70f461893e2c497510a90c0df4","target":"graph","created_at":"2026-07-05T06:26:35Z","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/2307.00066/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Due to effective pattern mining and feature representation, neural forecasting models based on deep learning have achieved great progress. The premise of effective learning is to collect sufficient data. However, in time series forecasting, it is difficult to obtain enough data, which limits the performance of neural forecasting models. To alleviate the data scarcity limitation, we design Sequence Decomposition Adaptation Network (SeDAN) which is a novel transfer architecture to improve forecasting performance on the target domain by aligning transferable knowledge from cross-domain datasets. ","authors_text":"Qiang Wang, Yan Gao, Yan Wang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-06-30T18:12:22Z","title":"Improving the Transferability of Time Series Forecasting with Decomposition Adaptation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.00066","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":"sha256:29b978ae5b8a8a633d4bace74b3bdeea790672c163aedfcff57a060fdf502783","target":"record","created_at":"2026-07-05T06:26:35Z","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":"4a2a8fec922d0bed7d234a3c56d42be0c3b3f77709738f7092cce3840f896de5","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-06-30T18:12:22Z","title_canon_sha256":"d39e3336f6791c7d64a416c24cd7950d82ebf1dfc7881cfe91d693601ab75b20"},"schema_version":"1.0","source":{"id":"2307.00066","kind":"arxiv","version":1}},"canonical_sha256":"b2ef96d6b41af7b5c7ca4858dd036ca6ebbcc8a8154dac5555a7f8f58f07578f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b2ef96d6b41af7b5c7ca4858dd036ca6ebbcc8a8154dac5555a7f8f58f07578f","first_computed_at":"2026-07-05T06:26:35.010440Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:26:35.010440Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ATNFRlvS3AXJJMmrFQO/KnEKqIjaBOz3VcLBpkn8ZUJ8njyo5LRzYsRfnKKdg1ChXUV9En2KlQTHJtYXzI8HAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T06:26:35.010806Z","signed_message":"canonical_sha256_bytes"},"source_id":"2307.00066","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:29b978ae5b8a8a633d4bace74b3bdeea790672c163aedfcff57a060fdf502783","sha256:5cb0a3a36fd684d35f4c3d423bf7037bac857c70f461893e2c497510a90c0df4"],"state_sha256":"f6b8570072dbc897f95007407a18a2bc966c271df55843c201b1f77594d49e6b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RNW7EhUOkYesCoproI4dZQ59Sxl5YaXVRDlvi+zDFXuEiaX1ogPH7WgcHCaqTNp2VWaDxUeqfTwXo0e3apJBAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T11:15:57.225738Z","bundle_sha256":"8394d534b6cdfa14a9316fba369aa1da500e48b7631b1322dde575bc14e330ba"}}