{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:HUWRTDXL6USLK6BTBZJEH3MKQP","short_pith_number":"pith:HUWRTDXL","canonical_record":{"source":{"id":"2106.09305","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-06-17T08:15:04Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"89f679decb2cf4d49d6b6881dd53a3763254baa5209d62656058c5d0cc87c3a9","abstract_canon_sha256":"762e89434adaf26159b20c119228caa0ffcde12b6cdf7a5fcbbcf35a7008134a"},"schema_version":"1.0"},"canonical_sha256":"3d2d198eebf524b578330e5243ed8a83d1222cac27de296e3a3cc2157dbe3a6c","source":{"kind":"arxiv","id":"2106.09305","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2106.09305","created_at":"2026-07-05T05:06:07Z"},{"alias_kind":"arxiv_version","alias_value":"2106.09305v3","created_at":"2026-07-05T05:06:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2106.09305","created_at":"2026-07-05T05:06:07Z"},{"alias_kind":"pith_short_12","alias_value":"HUWRTDXL6USL","created_at":"2026-07-05T05:06:07Z"},{"alias_kind":"pith_short_16","alias_value":"HUWRTDXL6USLK6BT","created_at":"2026-07-05T05:06:07Z"},{"alias_kind":"pith_short_8","alias_value":"HUWRTDXL","created_at":"2026-07-05T05:06:07Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:HUWRTDXL6USLK6BTBZJEH3MKQP","target":"record","payload":{"canonical_record":{"source":{"id":"2106.09305","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-06-17T08:15:04Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"89f679decb2cf4d49d6b6881dd53a3763254baa5209d62656058c5d0cc87c3a9","abstract_canon_sha256":"762e89434adaf26159b20c119228caa0ffcde12b6cdf7a5fcbbcf35a7008134a"},"schema_version":"1.0"},"canonical_sha256":"3d2d198eebf524b578330e5243ed8a83d1222cac27de296e3a3cc2157dbe3a6c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:06:07.837257Z","signature_b64":"hzeIewJNXwk/jh2V/H+QgPH0mrAFhQMaRIFIPSdVgt5wh/iqGF1zrJB6ckYxwoDzI9okYkgXELpgSC2+IwDmDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3d2d198eebf524b578330e5243ed8a83d1222cac27de296e3a3cc2157dbe3a6c","last_reissued_at":"2026-07-05T05:06:07.836712Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:06:07.836712Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2106.09305","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":"2026-07-05T05:06:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xIRv+RsaJbZu1B0B4a7SBeMA7SI4HbRm65h+BVNuXfEwBWu0IcZ/ER4Uyv7d9N1R2ZiXyXiXeCLHpFhcBIvzDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T20:40:38.666417Z"},"content_sha256":"83863361203f1434ceb38d0766908cc1b3e7bdb6eceba34a9a91299391984cdd","schema_version":"1.0","event_id":"sha256:83863361203f1434ceb38d0766908cc1b3e7bdb6eceba34a9a91299391984cdd"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:HUWRTDXL6USLK6BTBZJEH3MKQP","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interaction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Ailing Zeng, Lingna Ma, Minhao Liu, Muxi Chen, Qiang Xu, Qiuxia Lai, Zhijian Xu","submitted_at":"2021-06-17T08:15:04Z","abstract_excerpt":"One unique property of time series is that the temporal relations are largely preserved after downsampling into two sub-sequences. By taking advantage of this property, we propose a novel neural network architecture that conducts sample convolution and interaction for temporal modeling and forecasting, named SCINet. Specifically, SCINet is a recursive downsample-convolve-interact architecture. In each layer, we use multiple convolutional filters to extract distinct yet valuable temporal features from the downsampled sub-sequences or features. By combining these rich features aggregated from mu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2106.09305","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/2106.09305/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":"2026-07-05T05:06:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HHYGNczoikDmk03FBZGw/3ntFiD39XfoNEY4QdBqV14394F0L4hFAFK9jyMk9qO0VYLQpuNmn0WAH8YDv2cDCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T20:40:38.666930Z"},"content_sha256":"ca4372f64905001055946568b4a23ffcf867dd5544e91cfef941a8464aed8209","schema_version":"1.0","event_id":"sha256:ca4372f64905001055946568b4a23ffcf867dd5544e91cfef941a8464aed8209"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/HUWRTDXL6USLK6BTBZJEH3MKQP/bundle.json","state_url":"https://pith.science/pith/HUWRTDXL6USLK6BTBZJEH3MKQP/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/HUWRTDXL6USLK6BTBZJEH3MKQP/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":"2026-08-15T20:40:38Z","links":{"resolver":"https://pith.science/pith/HUWRTDXL6USLK6BTBZJEH3MKQP","bundle":"https://pith.science/pith/HUWRTDXL6USLK6BTBZJEH3MKQP/bundle.json","state":"https://pith.science/pith/HUWRTDXL6USLK6BTBZJEH3MKQP/state.json","well_known_bundle":"https://pith.science/.well-known/pith/HUWRTDXL6USLK6BTBZJEH3MKQP/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:HUWRTDXL6USLK6BTBZJEH3MKQP","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":"762e89434adaf26159b20c119228caa0ffcde12b6cdf7a5fcbbcf35a7008134a","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-06-17T08:15:04Z","title_canon_sha256":"89f679decb2cf4d49d6b6881dd53a3763254baa5209d62656058c5d0cc87c3a9"},"schema_version":"1.0","source":{"id":"2106.09305","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2106.09305","created_at":"2026-07-05T05:06:07Z"},{"alias_kind":"arxiv_version","alias_value":"2106.09305v3","created_at":"2026-07-05T05:06:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2106.09305","created_at":"2026-07-05T05:06:07Z"},{"alias_kind":"pith_short_12","alias_value":"HUWRTDXL6USL","created_at":"2026-07-05T05:06:07Z"},{"alias_kind":"pith_short_16","alias_value":"HUWRTDXL6USLK6BT","created_at":"2026-07-05T05:06:07Z"},{"alias_kind":"pith_short_8","alias_value":"HUWRTDXL","created_at":"2026-07-05T05:06:07Z"}],"graph_snapshots":[{"event_id":"sha256:ca4372f64905001055946568b4a23ffcf867dd5544e91cfef941a8464aed8209","target":"graph","created_at":"2026-07-05T05:06:07Z","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/2106.09305/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"One unique property of time series is that the temporal relations are largely preserved after downsampling into two sub-sequences. By taking advantage of this property, we propose a novel neural network architecture that conducts sample convolution and interaction for temporal modeling and forecasting, named SCINet. Specifically, SCINet is a recursive downsample-convolve-interact architecture. In each layer, we use multiple convolutional filters to extract distinct yet valuable temporal features from the downsampled sub-sequences or features. By combining these rich features aggregated from mu","authors_text":"Ailing Zeng, Lingna Ma, Minhao Liu, Muxi Chen, Qiang Xu, Qiuxia Lai, Zhijian Xu","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-06-17T08:15:04Z","title":"SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interaction"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2106.09305","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":"sha256:83863361203f1434ceb38d0766908cc1b3e7bdb6eceba34a9a91299391984cdd","target":"record","created_at":"2026-07-05T05:06:07Z","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":"762e89434adaf26159b20c119228caa0ffcde12b6cdf7a5fcbbcf35a7008134a","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-06-17T08:15:04Z","title_canon_sha256":"89f679decb2cf4d49d6b6881dd53a3763254baa5209d62656058c5d0cc87c3a9"},"schema_version":"1.0","source":{"id":"2106.09305","kind":"arxiv","version":3}},"canonical_sha256":"3d2d198eebf524b578330e5243ed8a83d1222cac27de296e3a3cc2157dbe3a6c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3d2d198eebf524b578330e5243ed8a83d1222cac27de296e3a3cc2157dbe3a6c","first_computed_at":"2026-07-05T05:06:07.836712Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:06:07.836712Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"hzeIewJNXwk/jh2V/H+QgPH0mrAFhQMaRIFIPSdVgt5wh/iqGF1zrJB6ckYxwoDzI9okYkgXELpgSC2+IwDmDA==","signature_status":"signed_v1","signed_at":"2026-07-05T05:06:07.837257Z","signed_message":"canonical_sha256_bytes"},"source_id":"2106.09305","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:83863361203f1434ceb38d0766908cc1b3e7bdb6eceba34a9a91299391984cdd","sha256:ca4372f64905001055946568b4a23ffcf867dd5544e91cfef941a8464aed8209"],"state_sha256":"97a270626225aaf6bd6668325d9d63e52412c45f3919ce79c4769fc8be762190"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lRMOxd7WZQaTs+3Ad7nggC62BBABSe1r+zkaLTyMsGTFKNx5tqsRGxqSYn3jc/C+QTRySScs+3yS2aQR544TCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-15T20:40:38.671875Z","bundle_sha256":"999a82040acad9cfc8fd35486b5abc12fcd19eae57cecb3d330f0c6ecf8ba95b"}}