{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:LV6BLGYGXLHMMNZL56JP3XA4CT","short_pith_number":"pith:LV6BLGYG","canonical_record":{"source":{"id":"2207.01186","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-07-04T04:03:00Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"25ba36ee2dca3d1ee16bec6af8d0f0edc56e19c1f8e1f1e7a57f7a012c305d9a","abstract_canon_sha256":"8244c04fca37822abcb5eb5c12c8d7e18ba038416f935785c6661aefb62e2148"},"schema_version":"1.0"},"canonical_sha256":"5d7c159b06bacec6372bef92fddc1c14e98e60b659fd7793da426c3f277d5b90","source":{"kind":"arxiv","id":"2207.01186","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2207.01186","created_at":"2026-07-05T04:37:09Z"},{"alias_kind":"arxiv_version","alias_value":"2207.01186v1","created_at":"2026-07-05T04:37:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2207.01186","created_at":"2026-07-05T04:37:09Z"},{"alias_kind":"pith_short_12","alias_value":"LV6BLGYGXLHM","created_at":"2026-07-05T04:37:09Z"},{"alias_kind":"pith_short_16","alias_value":"LV6BLGYGXLHMMNZL","created_at":"2026-07-05T04:37:09Z"},{"alias_kind":"pith_short_8","alias_value":"LV6BLGYG","created_at":"2026-07-05T04:37:09Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:LV6BLGYGXLHMMNZL56JP3XA4CT","target":"record","payload":{"canonical_record":{"source":{"id":"2207.01186","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-07-04T04:03:00Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"25ba36ee2dca3d1ee16bec6af8d0f0edc56e19c1f8e1f1e7a57f7a012c305d9a","abstract_canon_sha256":"8244c04fca37822abcb5eb5c12c8d7e18ba038416f935785c6661aefb62e2148"},"schema_version":"1.0"},"canonical_sha256":"5d7c159b06bacec6372bef92fddc1c14e98e60b659fd7793da426c3f277d5b90","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:37:09.039605Z","signature_b64":"RGlcgL+pYMemoq5xOJ6k7Ub87BoFfvWiShHXcnTKS+26LGJlpOphny8FkutoxW2JH+FOkGnraIkCc7YDVRyyCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5d7c159b06bacec6372bef92fddc1c14e98e60b659fd7793da426c3f277d5b90","last_reissued_at":"2026-07-05T04:37:09.039219Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:37:09.039219Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2207.01186","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":"2026-07-05T04:37:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bPLiT4/+iB7dgX6aDoWZwuGcxU8WCTS5tMcmSPbrgpjbcONwLdquyoVMgLqH4ZnjT6UFff3N0MJT2jQim1WSDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T17:16:51.726618Z"},"content_sha256":"c6e11d5db9f39fc02cb900ddcaef235cc1895e04f444d0927bec102abe376baa","schema_version":"1.0","event_id":"sha256:c6e11d5db9f39fc02cb900ddcaef235cc1895e04f444d0927bec102abe376baa"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:LV6BLGYGXLHMMNZL56JP3XA4CT","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Less Is More: Fast Multivariate Time Series Forecasting with Light Sampling-oriented MLP Structures","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Jiang Bian, Jian Li, Shun Zheng, Tianping Zhang, Wei Cao, Xiaohan Yi, Yizhuo Zhang","submitted_at":"2022-07-04T04:03:00Z","abstract_excerpt":"Multivariate time series forecasting has seen widely ranging applications in various domains, including finance, traffic, energy, and healthcare. To capture the sophisticated temporal patterns, plenty of research studies designed complex neural network architectures based on many variants of RNNs, GNNs, and Transformers. However, complex models are often computationally expensive and thus face a severe challenge in training and inference efficiency when applied to large-scale real-world datasets. In this paper, we introduce LightTS, a light deep learning architecture merely based on simple MLP"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2207.01186","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/2207.01186/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":"2026-07-05T04:37:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"IPmN6GepWlZMLP2yIovlSgTUYD4ngbA6EJGM/FQJhyZWbrPwyJ+0oUQ/SOOVFXvhgYyY7el2D7OVPXLSYAaCAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T17:16:51.727452Z"},"content_sha256":"eb49aec045fb5fe570cdc2692055e3306ffa7896299676d9037d2d1c8c77ba3a","schema_version":"1.0","event_id":"sha256:eb49aec045fb5fe570cdc2692055e3306ffa7896299676d9037d2d1c8c77ba3a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/LV6BLGYGXLHMMNZL56JP3XA4CT/bundle.json","state_url":"https://pith.science/pith/LV6BLGYGXLHMMNZL56JP3XA4CT/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/LV6BLGYGXLHMMNZL56JP3XA4CT/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":"2026-08-15T17:16:51Z","links":{"resolver":"https://pith.science/pith/LV6BLGYGXLHMMNZL56JP3XA4CT","bundle":"https://pith.science/pith/LV6BLGYGXLHMMNZL56JP3XA4CT/bundle.json","state":"https://pith.science/pith/LV6BLGYGXLHMMNZL56JP3XA4CT/state.json","well_known_bundle":"https://pith.science/.well-known/pith/LV6BLGYGXLHMMNZL56JP3XA4CT/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:LV6BLGYGXLHMMNZL56JP3XA4CT","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":"8244c04fca37822abcb5eb5c12c8d7e18ba038416f935785c6661aefb62e2148","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-07-04T04:03:00Z","title_canon_sha256":"25ba36ee2dca3d1ee16bec6af8d0f0edc56e19c1f8e1f1e7a57f7a012c305d9a"},"schema_version":"1.0","source":{"id":"2207.01186","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2207.01186","created_at":"2026-07-05T04:37:09Z"},{"alias_kind":"arxiv_version","alias_value":"2207.01186v1","created_at":"2026-07-05T04:37:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2207.01186","created_at":"2026-07-05T04:37:09Z"},{"alias_kind":"pith_short_12","alias_value":"LV6BLGYGXLHM","created_at":"2026-07-05T04:37:09Z"},{"alias_kind":"pith_short_16","alias_value":"LV6BLGYGXLHMMNZL","created_at":"2026-07-05T04:37:09Z"},{"alias_kind":"pith_short_8","alias_value":"LV6BLGYG","created_at":"2026-07-05T04:37:09Z"}],"graph_snapshots":[{"event_id":"sha256:eb49aec045fb5fe570cdc2692055e3306ffa7896299676d9037d2d1c8c77ba3a","target":"graph","created_at":"2026-07-05T04:37:09Z","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/2207.01186/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Multivariate time series forecasting has seen widely ranging applications in various domains, including finance, traffic, energy, and healthcare. To capture the sophisticated temporal patterns, plenty of research studies designed complex neural network architectures based on many variants of RNNs, GNNs, and Transformers. However, complex models are often computationally expensive and thus face a severe challenge in training and inference efficiency when applied to large-scale real-world datasets. In this paper, we introduce LightTS, a light deep learning architecture merely based on simple MLP","authors_text":"Jiang Bian, Jian Li, Shun Zheng, Tianping Zhang, Wei Cao, Xiaohan Yi, Yizhuo Zhang","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-07-04T04:03:00Z","title":"Less Is More: Fast Multivariate Time Series Forecasting with Light Sampling-oriented MLP Structures"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2207.01186","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:c6e11d5db9f39fc02cb900ddcaef235cc1895e04f444d0927bec102abe376baa","target":"record","created_at":"2026-07-05T04:37:09Z","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":"8244c04fca37822abcb5eb5c12c8d7e18ba038416f935785c6661aefb62e2148","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-07-04T04:03:00Z","title_canon_sha256":"25ba36ee2dca3d1ee16bec6af8d0f0edc56e19c1f8e1f1e7a57f7a012c305d9a"},"schema_version":"1.0","source":{"id":"2207.01186","kind":"arxiv","version":1}},"canonical_sha256":"5d7c159b06bacec6372bef92fddc1c14e98e60b659fd7793da426c3f277d5b90","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5d7c159b06bacec6372bef92fddc1c14e98e60b659fd7793da426c3f277d5b90","first_computed_at":"2026-07-05T04:37:09.039219Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:37:09.039219Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"RGlcgL+pYMemoq5xOJ6k7Ub87BoFfvWiShHXcnTKS+26LGJlpOphny8FkutoxW2JH+FOkGnraIkCc7YDVRyyCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T04:37:09.039605Z","signed_message":"canonical_sha256_bytes"},"source_id":"2207.01186","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c6e11d5db9f39fc02cb900ddcaef235cc1895e04f444d0927bec102abe376baa","sha256:eb49aec045fb5fe570cdc2692055e3306ffa7896299676d9037d2d1c8c77ba3a"],"state_sha256":"41d83c1846591cea1ac8d0b37de83516090b982215cfef75294cc889582171b8"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1FSpf6HtrXxPjqWYqfi/j/aBDtcbHjbdzhVvnhOmn6PnVDxtpb1X04tVM6U/9Ok7XzUKvsd8yfJgfG4dexWJCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-15T17:16:51.734934Z","bundle_sha256":"169580cb2425148c4d0a5abbecc7cf63160dc93a885949b3b5f72c783a80269d"}}