{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:RE4EZKC2V43QTPRYNR5RTEXD2H","short_pith_number":"pith:RE4EZKC2","canonical_record":{"source":{"id":"2405.07836","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-05-13T15:22:15Z","cross_cats_sorted":["stat.ME"],"title_canon_sha256":"f1e56759bd64a1aef4f80bddfd4eb84c034d2cc1bf822b6e1f2ab9278b343862","abstract_canon_sha256":"2cb3535e6d84d88114e9cf5f4291ecb08a64095a51f7d542a22ea8af1a6a0664"},"schema_version":"1.0"},"canonical_sha256":"89384ca85aaf3709be386c7b1992e3d1c931926eee89239eeaacef7f108e72fe","source":{"kind":"arxiv","id":"2405.07836","version":5},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2405.07836","created_at":"2026-06-01T02:03:17Z"},{"alias_kind":"arxiv_version","alias_value":"2405.07836v5","created_at":"2026-06-01T02:03:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.07836","created_at":"2026-06-01T02:03:17Z"},{"alias_kind":"pith_short_12","alias_value":"RE4EZKC2V43Q","created_at":"2026-06-01T02:03:17Z"},{"alias_kind":"pith_short_16","alias_value":"RE4EZKC2V43QTPRY","created_at":"2026-06-01T02:03:17Z"},{"alias_kind":"pith_short_8","alias_value":"RE4EZKC2","created_at":"2026-06-01T02:03:17Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:RE4EZKC2V43QTPRYNR5RTEXD2H","target":"record","payload":{"canonical_record":{"source":{"id":"2405.07836","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-05-13T15:22:15Z","cross_cats_sorted":["stat.ME"],"title_canon_sha256":"f1e56759bd64a1aef4f80bddfd4eb84c034d2cc1bf822b6e1f2ab9278b343862","abstract_canon_sha256":"2cb3535e6d84d88114e9cf5f4291ecb08a64095a51f7d542a22ea8af1a6a0664"},"schema_version":"1.0"},"canonical_sha256":"89384ca85aaf3709be386c7b1992e3d1c931926eee89239eeaacef7f108e72fe","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-01T02:03:17.645759Z","signature_b64":"Wlcdr2flnLuMAED+WS/TsAh/wztVX0mrXrpE4mK/Y+YpVkDXIv12vK0aY1u3M7FfYAAmSChsa/fJXgHIvxIjAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"89384ca85aaf3709be386c7b1992e3d1c931926eee89239eeaacef7f108e72fe","last_reissued_at":"2026-06-01T02:03:17.644721Z","signature_status":"signed_v1","first_computed_at":"2026-06-01T02:03:17.644721Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2405.07836","source_version":5,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-06-01T02:03:17Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"u9eorppBUI0eEJgZQ57ZjUb/8NqGX6L6cW2Dy1Y0VzvvRka5ywOyKXA6X00m0Z/+tZPpHN+4Sj0/PDTuDV/rAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-01T22:59:24.807219Z"},"content_sha256":"5826f95ecc29ace57fee9edc5a6b5c6cd3bece67f93e9090f08a0291349e91f0","schema_version":"1.0","event_id":"sha256:5826f95ecc29ace57fee9edc5a6b5c6cd3bece67f93e9090f08a0291349e91f0"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:RE4EZKC2V43QTPRYNR5RTEXD2H","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Forecasting with Hyper-Trees","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ME"],"primary_cat":"cs.LG","authors_text":"Alexander M\\\"arz, Kashif Rasul","submitted_at":"2024-05-13T15:22:15Z","abstract_excerpt":"We introduce Hyper-Trees as a novel framework for modeling time series data using gradient boosted trees. Unlike conventional tree-based approaches that forecast time series directly, Hyper-Trees learn the parameters of a target time series model, such as ARIMA or Exponential Smoothing, as functions of features. These parameters are then used by the target model to generate the final forecasts. Our framework combines the effectiveness of decision trees on tabular data with classical forecasting models, thereby inducing a time series inductive bias into tree-based models. To resolve the scaling"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.07836","kind":"arxiv","version":5},"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/2405.07836/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":"2026-06-01T02:03:17Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"s8pnvt62m7RFqm6sqkoKZN8+3DjTjX3IMpTghW4E6op8Gy6Zrh4AMw9Fm2NPSbvgae00EuuhyD16t1BKmvaPAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-01T22:59:24.807889Z"},"content_sha256":"19b2bf1bdf9b1029b53bec420694a735f4b494b3616a2606102e85e7868ce6a1","schema_version":"1.0","event_id":"sha256:19b2bf1bdf9b1029b53bec420694a735f4b494b3616a2606102e85e7868ce6a1"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/RE4EZKC2V43QTPRYNR5RTEXD2H/bundle.json","state_url":"https://pith.science/pith/RE4EZKC2V43QTPRYNR5RTEXD2H/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/RE4EZKC2V43QTPRYNR5RTEXD2H/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":"2026-07-01T22:59:24Z","links":{"resolver":"https://pith.science/pith/RE4EZKC2V43QTPRYNR5RTEXD2H","bundle":"https://pith.science/pith/RE4EZKC2V43QTPRYNR5RTEXD2H/bundle.json","state":"https://pith.science/pith/RE4EZKC2V43QTPRYNR5RTEXD2H/state.json","well_known_bundle":"https://pith.science/.well-known/pith/RE4EZKC2V43QTPRYNR5RTEXD2H/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:RE4EZKC2V43QTPRYNR5RTEXD2H","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":"2cb3535e6d84d88114e9cf5f4291ecb08a64095a51f7d542a22ea8af1a6a0664","cross_cats_sorted":["stat.ME"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-05-13T15:22:15Z","title_canon_sha256":"f1e56759bd64a1aef4f80bddfd4eb84c034d2cc1bf822b6e1f2ab9278b343862"},"schema_version":"1.0","source":{"id":"2405.07836","kind":"arxiv","version":5}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2405.07836","created_at":"2026-06-01T02:03:17Z"},{"alias_kind":"arxiv_version","alias_value":"2405.07836v5","created_at":"2026-06-01T02:03:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.07836","created_at":"2026-06-01T02:03:17Z"},{"alias_kind":"pith_short_12","alias_value":"RE4EZKC2V43Q","created_at":"2026-06-01T02:03:17Z"},{"alias_kind":"pith_short_16","alias_value":"RE4EZKC2V43QTPRY","created_at":"2026-06-01T02:03:17Z"},{"alias_kind":"pith_short_8","alias_value":"RE4EZKC2","created_at":"2026-06-01T02:03:17Z"}],"graph_snapshots":[{"event_id":"sha256:19b2bf1bdf9b1029b53bec420694a735f4b494b3616a2606102e85e7868ce6a1","target":"graph","created_at":"2026-06-01T02:03:17Z","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/2405.07836/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We introduce Hyper-Trees as a novel framework for modeling time series data using gradient boosted trees. Unlike conventional tree-based approaches that forecast time series directly, Hyper-Trees learn the parameters of a target time series model, such as ARIMA or Exponential Smoothing, as functions of features. These parameters are then used by the target model to generate the final forecasts. Our framework combines the effectiveness of decision trees on tabular data with classical forecasting models, thereby inducing a time series inductive bias into tree-based models. To resolve the scaling","authors_text":"Alexander M\\\"arz, Kashif Rasul","cross_cats":["stat.ME"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-05-13T15:22:15Z","title":"Forecasting with Hyper-Trees"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.07836","kind":"arxiv","version":5},"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":"sha256:5826f95ecc29ace57fee9edc5a6b5c6cd3bece67f93e9090f08a0291349e91f0","target":"record","created_at":"2026-06-01T02:03:17Z","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":"2cb3535e6d84d88114e9cf5f4291ecb08a64095a51f7d542a22ea8af1a6a0664","cross_cats_sorted":["stat.ME"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-05-13T15:22:15Z","title_canon_sha256":"f1e56759bd64a1aef4f80bddfd4eb84c034d2cc1bf822b6e1f2ab9278b343862"},"schema_version":"1.0","source":{"id":"2405.07836","kind":"arxiv","version":5}},"canonical_sha256":"89384ca85aaf3709be386c7b1992e3d1c931926eee89239eeaacef7f108e72fe","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"89384ca85aaf3709be386c7b1992e3d1c931926eee89239eeaacef7f108e72fe","first_computed_at":"2026-06-01T02:03:17.644721Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-01T02:03:17.644721Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Wlcdr2flnLuMAED+WS/TsAh/wztVX0mrXrpE4mK/Y+YpVkDXIv12vK0aY1u3M7FfYAAmSChsa/fJXgHIvxIjAw==","signature_status":"signed_v1","signed_at":"2026-06-01T02:03:17.645759Z","signed_message":"canonical_sha256_bytes"},"source_id":"2405.07836","source_kind":"arxiv","source_version":5}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5826f95ecc29ace57fee9edc5a6b5c6cd3bece67f93e9090f08a0291349e91f0","sha256:19b2bf1bdf9b1029b53bec420694a735f4b494b3616a2606102e85e7868ce6a1"],"state_sha256":"87e109cc7a098e22159a3f225eef69aba14d701d6cd6dc0139a5192806fb810f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Y+wBlI/J0RyDJzh2Estom1DdA8SM4Lx8fypKosbFTFKgjbL/0a4aB8vEEkHBGd3V1TC1MvKh6dumHU0ry/aVCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-01T22:59:24.811153Z","bundle_sha256":"f8e12489d13bf3470d053d3a5d54be56dfc1a17c0cc686f4c96d68f57cb0eb49"}}