{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:IOQ3EW5NPYZC7ACAC7VWALUCOM","short_pith_number":"pith:IOQ3EW5N","canonical_record":{"source":{"id":"1805.04193","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-05-10T22:17:51Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"a4d66399ae34e391a019c3c9577be7245601a310b19d8e7bcd74fe29772f0b1a","abstract_canon_sha256":"1cc49037d4834580420eb72fa9382670a3434caa232182c63c08b413e75b603c"},"schema_version":"1.0"},"canonical_sha256":"43a1b25bad7e322f804017eb602e827320e35593d1b943aada5694bc581070a3","source":{"kind":"arxiv","id":"1805.04193","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1805.04193","created_at":"2026-05-18T00:16:13Z"},{"alias_kind":"arxiv_version","alias_value":"1805.04193v1","created_at":"2026-05-18T00:16:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.04193","created_at":"2026-05-18T00:16:13Z"},{"alias_kind":"pith_short_12","alias_value":"IOQ3EW5NPYZC","created_at":"2026-05-18T12:32:31Z"},{"alias_kind":"pith_short_16","alias_value":"IOQ3EW5NPYZC7ACA","created_at":"2026-05-18T12:32:31Z"},{"alias_kind":"pith_short_8","alias_value":"IOQ3EW5N","created_at":"2026-05-18T12:32:31Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:IOQ3EW5NPYZC7ACAC7VWALUCOM","target":"record","payload":{"canonical_record":{"source":{"id":"1805.04193","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-05-10T22:17:51Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"a4d66399ae34e391a019c3c9577be7245601a310b19d8e7bcd74fe29772f0b1a","abstract_canon_sha256":"1cc49037d4834580420eb72fa9382670a3434caa232182c63c08b413e75b603c"},"schema_version":"1.0"},"canonical_sha256":"43a1b25bad7e322f804017eb602e827320e35593d1b943aada5694bc581070a3","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:16:13.309437Z","signature_b64":"5Ues94GwECnGOk77FL7uIpwPaW5BBkieLlz3J40DkIzNf3lF0lgz8h+e6x4PvAZLPu3jaS1K01HCXyV2lwwZCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"43a1b25bad7e322f804017eb602e827320e35593d1b943aada5694bc581070a3","last_reissued_at":"2026-05-18T00:16:13.308758Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:16:13.308758Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1805.04193","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":"2026-05-18T00:16:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gAcYuiJsFDUyx8ZnELfFWUhslEz5hPMO77mygPjuvayyU3DWcLLxY+iUJsmLaiChm9vM7hQ+bhQYFFUeGDcsCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T11:24:07.942663Z"},"content_sha256":"8fde2310decb70dd37de5e022282100ea6ca2c1b4ac51078fa59d3ce97c67b28","schema_version":"1.0","event_id":"sha256:8fde2310decb70dd37de5e022282100ea6ca2c1b4ac51078fa59d3ce97c67b28"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:IOQ3EW5NPYZC7ACAC7VWALUCOM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"An Unsupervised Clustering-Based Short-Term Solar Forecasting Methodology Using Multi-Model Machine Learning Blending","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Bri-Mathias Hodge, Cong Feng, Hendrik F. Hamann, Jie Zhang, Mingjian Cui, Siyuan Lu","submitted_at":"2018-05-10T22:17:51Z","abstract_excerpt":"Solar forecasting accuracy is affected by weather conditions, and weather awareness forecasting models are expected to improve the performance. However, it may not be available and reliable to classify different forecasting tasks by using only meteorological weather categorization. In this paper, an unsupervised clustering-based (UC-based) solar forecasting methodology is developed for short-term (1-hour-ahead) global horizontal irradiance (GHI) forecasting. This methodology consists of three parts: GHI time series unsupervised clustering, pattern recognition, and UC-based forecasting. The dai"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.04193","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":""},"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":"2026-05-18T00:16:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"u61dCfxv+a4/+tGFjuEI7rf/1CH0/nSVnwFY5Np81VqxCeewvBvAePm67mAG1zj4qaTFnpRFFWbqn/cbHa6XBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T11:24:07.943252Z"},"content_sha256":"fe80245f2855a1f4caf6f18030542a031b7be65532fe19b0f8d3df855d1257b6","schema_version":"1.0","event_id":"sha256:fe80245f2855a1f4caf6f18030542a031b7be65532fe19b0f8d3df855d1257b6"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/IOQ3EW5NPYZC7ACAC7VWALUCOM/bundle.json","state_url":"https://pith.science/pith/IOQ3EW5NPYZC7ACAC7VWALUCOM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/IOQ3EW5NPYZC7ACAC7VWALUCOM/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":"2026-05-31T11:24:07Z","links":{"resolver":"https://pith.science/pith/IOQ3EW5NPYZC7ACAC7VWALUCOM","bundle":"https://pith.science/pith/IOQ3EW5NPYZC7ACAC7VWALUCOM/bundle.json","state":"https://pith.science/pith/IOQ3EW5NPYZC7ACAC7VWALUCOM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/IOQ3EW5NPYZC7ACAC7VWALUCOM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:IOQ3EW5NPYZC7ACAC7VWALUCOM","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":"1cc49037d4834580420eb72fa9382670a3434caa232182c63c08b413e75b603c","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-05-10T22:17:51Z","title_canon_sha256":"a4d66399ae34e391a019c3c9577be7245601a310b19d8e7bcd74fe29772f0b1a"},"schema_version":"1.0","source":{"id":"1805.04193","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1805.04193","created_at":"2026-05-18T00:16:13Z"},{"alias_kind":"arxiv_version","alias_value":"1805.04193v1","created_at":"2026-05-18T00:16:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.04193","created_at":"2026-05-18T00:16:13Z"},{"alias_kind":"pith_short_12","alias_value":"IOQ3EW5NPYZC","created_at":"2026-05-18T12:32:31Z"},{"alias_kind":"pith_short_16","alias_value":"IOQ3EW5NPYZC7ACA","created_at":"2026-05-18T12:32:31Z"},{"alias_kind":"pith_short_8","alias_value":"IOQ3EW5N","created_at":"2026-05-18T12:32:31Z"}],"graph_snapshots":[{"event_id":"sha256:fe80245f2855a1f4caf6f18030542a031b7be65532fe19b0f8d3df855d1257b6","target":"graph","created_at":"2026-05-18T00:16:13Z","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"},"paper":{"abstract_excerpt":"Solar forecasting accuracy is affected by weather conditions, and weather awareness forecasting models are expected to improve the performance. However, it may not be available and reliable to classify different forecasting tasks by using only meteorological weather categorization. In this paper, an unsupervised clustering-based (UC-based) solar forecasting methodology is developed for short-term (1-hour-ahead) global horizontal irradiance (GHI) forecasting. This methodology consists of three parts: GHI time series unsupervised clustering, pattern recognition, and UC-based forecasting. The dai","authors_text":"Bri-Mathias Hodge, Cong Feng, Hendrik F. Hamann, Jie Zhang, Mingjian Cui, Siyuan Lu","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-05-10T22:17:51Z","title":"An Unsupervised Clustering-Based Short-Term Solar Forecasting Methodology Using Multi-Model Machine Learning Blending"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.04193","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":"sha256:8fde2310decb70dd37de5e022282100ea6ca2c1b4ac51078fa59d3ce97c67b28","target":"record","created_at":"2026-05-18T00:16:13Z","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":"1cc49037d4834580420eb72fa9382670a3434caa232182c63c08b413e75b603c","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-05-10T22:17:51Z","title_canon_sha256":"a4d66399ae34e391a019c3c9577be7245601a310b19d8e7bcd74fe29772f0b1a"},"schema_version":"1.0","source":{"id":"1805.04193","kind":"arxiv","version":1}},"canonical_sha256":"43a1b25bad7e322f804017eb602e827320e35593d1b943aada5694bc581070a3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"43a1b25bad7e322f804017eb602e827320e35593d1b943aada5694bc581070a3","first_computed_at":"2026-05-18T00:16:13.308758Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:16:13.308758Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"5Ues94GwECnGOk77FL7uIpwPaW5BBkieLlz3J40DkIzNf3lF0lgz8h+e6x4PvAZLPu3jaS1K01HCXyV2lwwZCg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:16:13.309437Z","signed_message":"canonical_sha256_bytes"},"source_id":"1805.04193","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8fde2310decb70dd37de5e022282100ea6ca2c1b4ac51078fa59d3ce97c67b28","sha256:fe80245f2855a1f4caf6f18030542a031b7be65532fe19b0f8d3df855d1257b6"],"state_sha256":"6de35dd9b86e11b6c419b0e7999774c81aa8800be803c23a973787651cb8cda4"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tAUz/pFx50me1nYiaAEU6EYVjDLzLvtPkKIlyADbPI9R9Epe7o1qLoJeQOZ3K3mWJOX8/0J6SxF+MtHA7SZyAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-31T11:24:07.947041Z","bundle_sha256":"5912d24fde7354771ed4989f752416c257549893338277d2ad0872a05a59669a"}}