{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:VY7643PBRID3NW2ZVODTFH5MCG","short_pith_number":"pith:VY7643PB","canonical_record":{"source":{"id":"2303.12316","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-03-22T05:14:36Z","cross_cats_sorted":[],"title_canon_sha256":"76e5a379479899e5835cc8bdfb06aec0505909e1b6001d63175f332d66a4ea08","abstract_canon_sha256":"d48d3d4635a730f6dc49c96c844940dfd8337f906fb351faedd7df4d4ad65986"},"schema_version":"1.0"},"canonical_sha256":"ae3fee6de18a07b6db59ab87329fac119467c537ce7714096107c53277a0dc6e","source":{"kind":"arxiv","id":"2303.12316","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2303.12316","created_at":"2026-07-05T05:53:39Z"},{"alias_kind":"arxiv_version","alias_value":"2303.12316v1","created_at":"2026-07-05T05:53:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.12316","created_at":"2026-07-05T05:53:39Z"},{"alias_kind":"pith_short_12","alias_value":"VY7643PBRID3","created_at":"2026-07-05T05:53:39Z"},{"alias_kind":"pith_short_16","alias_value":"VY7643PBRID3NW2Z","created_at":"2026-07-05T05:53:39Z"},{"alias_kind":"pith_short_8","alias_value":"VY7643PB","created_at":"2026-07-05T05:53:39Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:VY7643PBRID3NW2ZVODTFH5MCG","target":"record","payload":{"canonical_record":{"source":{"id":"2303.12316","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-03-22T05:14:36Z","cross_cats_sorted":[],"title_canon_sha256":"76e5a379479899e5835cc8bdfb06aec0505909e1b6001d63175f332d66a4ea08","abstract_canon_sha256":"d48d3d4635a730f6dc49c96c844940dfd8337f906fb351faedd7df4d4ad65986"},"schema_version":"1.0"},"canonical_sha256":"ae3fee6de18a07b6db59ab87329fac119467c537ce7714096107c53277a0dc6e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:53:39.808623Z","signature_b64":"Z2W0LqFpoWCu4C7QDhTdYiaUsj5A0ZjKCv6LUr1Tt4+E4eFLGy/O7KOv8U81zLsU+Sb50QvBEaDWXPc0HLr+BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ae3fee6de18a07b6db59ab87329fac119467c537ce7714096107c53277a0dc6e","last_reissued_at":"2026-07-05T05:53:39.808105Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:53:39.808105Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2303.12316","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":"2026-07-05T05:53:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CQiaAqS0xQPXyjH2dwJQ6OCKjmIvwCBWWpmKompBkBBUMpSkm1jdZBVEa8ASKr4DTr25s+XU06+yEZ/D4juxBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T01:28:06.538300Z"},"content_sha256":"47825fc55fda034431180a80faaa31ed73078860e11294108f33a72210f278bd","schema_version":"1.0","event_id":"sha256:47825fc55fda034431180a80faaa31ed73078860e11294108f33a72210f278bd"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:VY7643PBRID3NW2ZVODTFH5MCG","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"TsSHAP: Robust model agnostic feature-based explainability for time series forecasting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Arindam Jati, Giridhar Ganapavarapu, Kanthi Sarpatwar, Nupur Aggarwal, Roman Vaculin, Sumanta Mukherjee, Vikas C. Raykar","submitted_at":"2023-03-22T05:14:36Z","abstract_excerpt":"A trustworthy machine learning model should be accurate as well as explainable. Understanding why a model makes a certain decision defines the notion of explainability. While various flavors of explainability have been well-studied in supervised learning paradigms like classification and regression, literature on explainability for time series forecasting is relatively scarce.\n  In this paper, we propose a feature-based explainability algorithm, TsSHAP, that can explain the forecast of any black-box forecasting model. The method is agnostic of the forecasting model and can provide explanations"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.12316","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/2303.12316/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":"2026-07-05T05:53:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"E95onvJ6jAuioSdFApnUQfoYyi917A3dMpwWc0U9ZlE6QJEgiVwYPjW4VV1XN0Tr33e8nAlMlS9nkRGYKMKYDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T01:28:06.538900Z"},"content_sha256":"1bf5c0cab4f1ab7097c16947287cadf4a1b3628fbdacc2de12d897ef8212d338","schema_version":"1.0","event_id":"sha256:1bf5c0cab4f1ab7097c16947287cadf4a1b3628fbdacc2de12d897ef8212d338"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/VY7643PBRID3NW2ZVODTFH5MCG/bundle.json","state_url":"https://pith.science/pith/VY7643PBRID3NW2ZVODTFH5MCG/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/VY7643PBRID3NW2ZVODTFH5MCG/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":"2026-08-04T01:28:06Z","links":{"resolver":"https://pith.science/pith/VY7643PBRID3NW2ZVODTFH5MCG","bundle":"https://pith.science/pith/VY7643PBRID3NW2ZVODTFH5MCG/bundle.json","state":"https://pith.science/pith/VY7643PBRID3NW2ZVODTFH5MCG/state.json","well_known_bundle":"https://pith.science/.well-known/pith/VY7643PBRID3NW2ZVODTFH5MCG/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:VY7643PBRID3NW2ZVODTFH5MCG","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":"d48d3d4635a730f6dc49c96c844940dfd8337f906fb351faedd7df4d4ad65986","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-03-22T05:14:36Z","title_canon_sha256":"76e5a379479899e5835cc8bdfb06aec0505909e1b6001d63175f332d66a4ea08"},"schema_version":"1.0","source":{"id":"2303.12316","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2303.12316","created_at":"2026-07-05T05:53:39Z"},{"alias_kind":"arxiv_version","alias_value":"2303.12316v1","created_at":"2026-07-05T05:53:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.12316","created_at":"2026-07-05T05:53:39Z"},{"alias_kind":"pith_short_12","alias_value":"VY7643PBRID3","created_at":"2026-07-05T05:53:39Z"},{"alias_kind":"pith_short_16","alias_value":"VY7643PBRID3NW2Z","created_at":"2026-07-05T05:53:39Z"},{"alias_kind":"pith_short_8","alias_value":"VY7643PB","created_at":"2026-07-05T05:53:39Z"}],"graph_snapshots":[{"event_id":"sha256:1bf5c0cab4f1ab7097c16947287cadf4a1b3628fbdacc2de12d897ef8212d338","target":"graph","created_at":"2026-07-05T05:53:39Z","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/2303.12316/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"A trustworthy machine learning model should be accurate as well as explainable. Understanding why a model makes a certain decision defines the notion of explainability. While various flavors of explainability have been well-studied in supervised learning paradigms like classification and regression, literature on explainability for time series forecasting is relatively scarce.\n  In this paper, we propose a feature-based explainability algorithm, TsSHAP, that can explain the forecast of any black-box forecasting model. The method is agnostic of the forecasting model and can provide explanations","authors_text":"Arindam Jati, Giridhar Ganapavarapu, Kanthi Sarpatwar, Nupur Aggarwal, Roman Vaculin, Sumanta Mukherjee, Vikas C. Raykar","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-03-22T05:14:36Z","title":"TsSHAP: Robust model agnostic feature-based explainability for time series forecasting"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.12316","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":"sha256:47825fc55fda034431180a80faaa31ed73078860e11294108f33a72210f278bd","target":"record","created_at":"2026-07-05T05:53:39Z","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":"d48d3d4635a730f6dc49c96c844940dfd8337f906fb351faedd7df4d4ad65986","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-03-22T05:14:36Z","title_canon_sha256":"76e5a379479899e5835cc8bdfb06aec0505909e1b6001d63175f332d66a4ea08"},"schema_version":"1.0","source":{"id":"2303.12316","kind":"arxiv","version":1}},"canonical_sha256":"ae3fee6de18a07b6db59ab87329fac119467c537ce7714096107c53277a0dc6e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ae3fee6de18a07b6db59ab87329fac119467c537ce7714096107c53277a0dc6e","first_computed_at":"2026-07-05T05:53:39.808105Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:53:39.808105Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Z2W0LqFpoWCu4C7QDhTdYiaUsj5A0ZjKCv6LUr1Tt4+E4eFLGy/O7KOv8U81zLsU+Sb50QvBEaDWXPc0HLr+BA==","signature_status":"signed_v1","signed_at":"2026-07-05T05:53:39.808623Z","signed_message":"canonical_sha256_bytes"},"source_id":"2303.12316","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:47825fc55fda034431180a80faaa31ed73078860e11294108f33a72210f278bd","sha256:1bf5c0cab4f1ab7097c16947287cadf4a1b3628fbdacc2de12d897ef8212d338"],"state_sha256":"be7d92532577f263c909dd512052b3dbde52df506d888646ee124bf17c0f9fb2"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"m/+ynSinTHx/QKADryMWk3g1uKqp6R3J7dqGywiu6QLPhrv3yyfIkFeQrJBQvVUrWDW9PPTUhjrXBdxoiBrXBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T01:28:06.542420Z","bundle_sha256":"560e3eadfe0d3cc7fa64f53c2c9fc97f4ea764b423bf08a779e073338db066c3"}}