{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:MIQ4OMWA2G3EL6UVFFZB56HYIP","short_pith_number":"pith:MIQ4OMWA","canonical_record":{"source":{"id":"2302.09292","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2023-02-18T11:25:42Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"df94ada6624c0b4dcfe6ea7b213987d1866810e5f19b201364a06164d7e947e1","abstract_canon_sha256":"b9a33da18309a557e6c2672e198b901a0ff835364c8d785f087031bca1ed824d"},"schema_version":"1.0"},"canonical_sha256":"6221c732c0d1b645fa9529721ef8f843e076f4cbfa0f04ccbe6c0398ecca29e5","source":{"kind":"arxiv","id":"2302.09292","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2302.09292","created_at":"2026-07-05T05:43:23Z"},{"alias_kind":"arxiv_version","alias_value":"2302.09292v1","created_at":"2026-07-05T05:43:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.09292","created_at":"2026-07-05T05:43:23Z"},{"alias_kind":"pith_short_12","alias_value":"MIQ4OMWA2G3E","created_at":"2026-07-05T05:43:23Z"},{"alias_kind":"pith_short_16","alias_value":"MIQ4OMWA2G3EL6UV","created_at":"2026-07-05T05:43:23Z"},{"alias_kind":"pith_short_8","alias_value":"MIQ4OMWA","created_at":"2026-07-05T05:43:23Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:MIQ4OMWA2G3EL6UVFFZB56HYIP","target":"record","payload":{"canonical_record":{"source":{"id":"2302.09292","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2023-02-18T11:25:42Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"df94ada6624c0b4dcfe6ea7b213987d1866810e5f19b201364a06164d7e947e1","abstract_canon_sha256":"b9a33da18309a557e6c2672e198b901a0ff835364c8d785f087031bca1ed824d"},"schema_version":"1.0"},"canonical_sha256":"6221c732c0d1b645fa9529721ef8f843e076f4cbfa0f04ccbe6c0398ecca29e5","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:43:23.723414Z","signature_b64":"I78kk053NqnFmmE+5OVlZZlAfHTCTxfv9KrST+4niWsIBQr1+A/12oLVLp5+iLi/hOQuBxMpCgDqTp0IfeQIBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6221c732c0d1b645fa9529721ef8f843e076f4cbfa0f04ccbe6c0398ecca29e5","last_reissued_at":"2026-07-05T05:43:23.723040Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:43:23.723040Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2302.09292","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":"2026-07-05T05:43:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"e8VRZ8hUabyTyqdAiUv+HLF4xnVYW78SfCgxyYuW87OV6EzHkrbYDBMnofb/WnJTs6ofwFl4KfnfJSsRYEpBDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T15:48:12.205503Z"},"content_sha256":"e65e51678604327b026f40969b62b39acf70c381a8969aea15ff1bc2a1b4ed1c","schema_version":"1.0","event_id":"sha256:e65e51678604327b026f40969b62b39acf70c381a8969aea15ff1bc2a1b4ed1c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:MIQ4OMWA2G3EL6UVFFZB56HYIP","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"FrAug: Frequency Domain Augmentation for Time Series Forecasting","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Ailing Zeng, Muxi Chen, Qiang Xu, Zhijian Xu","submitted_at":"2023-02-18T11:25:42Z","abstract_excerpt":"Data augmentation (DA) has become a de facto solution to expand training data size for deep learning. With the proliferation of deep models for time series analysis, various time series DA techniques are proposed in the literature, e.g., cropping-, warping-, flipping-, and mixup-based methods. However, these augmentation methods mainly apply to time series classification and anomaly detection tasks. In time series forecasting (TSF), we need to model the fine-grained temporal relationship within time series segments to generate accurate forecasting results given data in a look-back window. Exis"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.09292","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/2302.09292/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":"2026-07-05T05:43:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0cXQyHgSc0i+Pg4DYortdLiswBbtDIbEcO6BCZt6bGWtKwLzNNYmkDytqEOpopomHMsQGLah8RRB4EV6g0/EAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T15:48:12.206013Z"},"content_sha256":"146bd5f5a2452099147b4d4c39c942e2bff8e86f470759bb042baf3312efcd57","schema_version":"1.0","event_id":"sha256:146bd5f5a2452099147b4d4c39c942e2bff8e86f470759bb042baf3312efcd57"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/MIQ4OMWA2G3EL6UVFFZB56HYIP/bundle.json","state_url":"https://pith.science/pith/MIQ4OMWA2G3EL6UVFFZB56HYIP/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/MIQ4OMWA2G3EL6UVFFZB56HYIP/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":"2026-08-07T15:48:12Z","links":{"resolver":"https://pith.science/pith/MIQ4OMWA2G3EL6UVFFZB56HYIP","bundle":"https://pith.science/pith/MIQ4OMWA2G3EL6UVFFZB56HYIP/bundle.json","state":"https://pith.science/pith/MIQ4OMWA2G3EL6UVFFZB56HYIP/state.json","well_known_bundle":"https://pith.science/.well-known/pith/MIQ4OMWA2G3EL6UVFFZB56HYIP/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:MIQ4OMWA2G3EL6UVFFZB56HYIP","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":"b9a33da18309a557e6c2672e198b901a0ff835364c8d785f087031bca1ed824d","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2023-02-18T11:25:42Z","title_canon_sha256":"df94ada6624c0b4dcfe6ea7b213987d1866810e5f19b201364a06164d7e947e1"},"schema_version":"1.0","source":{"id":"2302.09292","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2302.09292","created_at":"2026-07-05T05:43:23Z"},{"alias_kind":"arxiv_version","alias_value":"2302.09292v1","created_at":"2026-07-05T05:43:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.09292","created_at":"2026-07-05T05:43:23Z"},{"alias_kind":"pith_short_12","alias_value":"MIQ4OMWA2G3E","created_at":"2026-07-05T05:43:23Z"},{"alias_kind":"pith_short_16","alias_value":"MIQ4OMWA2G3EL6UV","created_at":"2026-07-05T05:43:23Z"},{"alias_kind":"pith_short_8","alias_value":"MIQ4OMWA","created_at":"2026-07-05T05:43:23Z"}],"graph_snapshots":[{"event_id":"sha256:146bd5f5a2452099147b4d4c39c942e2bff8e86f470759bb042baf3312efcd57","target":"graph","created_at":"2026-07-05T05:43:23Z","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/2302.09292/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Data augmentation (DA) has become a de facto solution to expand training data size for deep learning. With the proliferation of deep models for time series analysis, various time series DA techniques are proposed in the literature, e.g., cropping-, warping-, flipping-, and mixup-based methods. However, these augmentation methods mainly apply to time series classification and anomaly detection tasks. In time series forecasting (TSF), we need to model the fine-grained temporal relationship within time series segments to generate accurate forecasting results given data in a look-back window. Exis","authors_text":"Ailing Zeng, Muxi Chen, Qiang Xu, Zhijian Xu","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2023-02-18T11:25:42Z","title":"FrAug: Frequency Domain Augmentation for Time Series Forecasting"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.09292","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:e65e51678604327b026f40969b62b39acf70c381a8969aea15ff1bc2a1b4ed1c","target":"record","created_at":"2026-07-05T05:43:23Z","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":"b9a33da18309a557e6c2672e198b901a0ff835364c8d785f087031bca1ed824d","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2023-02-18T11:25:42Z","title_canon_sha256":"df94ada6624c0b4dcfe6ea7b213987d1866810e5f19b201364a06164d7e947e1"},"schema_version":"1.0","source":{"id":"2302.09292","kind":"arxiv","version":1}},"canonical_sha256":"6221c732c0d1b645fa9529721ef8f843e076f4cbfa0f04ccbe6c0398ecca29e5","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6221c732c0d1b645fa9529721ef8f843e076f4cbfa0f04ccbe6c0398ecca29e5","first_computed_at":"2026-07-05T05:43:23.723040Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:43:23.723040Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"I78kk053NqnFmmE+5OVlZZlAfHTCTxfv9KrST+4niWsIBQr1+A/12oLVLp5+iLi/hOQuBxMpCgDqTp0IfeQIBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T05:43:23.723414Z","signed_message":"canonical_sha256_bytes"},"source_id":"2302.09292","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e65e51678604327b026f40969b62b39acf70c381a8969aea15ff1bc2a1b4ed1c","sha256:146bd5f5a2452099147b4d4c39c942e2bff8e86f470759bb042baf3312efcd57"],"state_sha256":"2cada626c5f06db41133d2874d2dd0a09c28dd8ba58824102c219b28874965e7"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nhwpgOfeyMJqDL0r3AjfCng5zYCwol4u1f7cexiwpBdFWsjZywxd/HVbWPJqMjB4IJDAlEwSEM5laO9lvppHAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T15:48:12.237481Z","bundle_sha256":"deb6897775fbdeeaba9610e934879eae692703c31fcee5afad0bab981c19da95"}}