{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:6IFNUX6KPOWTXYEWH5LIERLR3P","short_pith_number":"pith:6IFNUX6K","canonical_record":{"source":{"id":"1906.04818","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-06-11T20:58:25Z","cross_cats_sorted":["cs.LG","cs.NE"],"title_canon_sha256":"900599a30eb54567305495390e0c227f99845b29e7892af7c085cf957db087f1","abstract_canon_sha256":"ba246b55850e556d3c5826d75a927dfd734d2d6e517fe9e6bfd2416a44b3dc3e"},"schema_version":"1.0"},"canonical_sha256":"f20ada5fca7bad3be0963f56824571dbf395350e7a1dab1c57d616d820d43cc6","source":{"kind":"arxiv","id":"1906.04818","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.04818","created_at":"2026-05-17T23:43:30Z"},{"alias_kind":"arxiv_version","alias_value":"1906.04818v1","created_at":"2026-05-17T23:43:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.04818","created_at":"2026-05-17T23:43:30Z"},{"alias_kind":"pith_short_12","alias_value":"6IFNUX6KPOWT","created_at":"2026-05-18T12:33:10Z"},{"alias_kind":"pith_short_16","alias_value":"6IFNUX6KPOWTXYEW","created_at":"2026-05-18T12:33:10Z"},{"alias_kind":"pith_short_8","alias_value":"6IFNUX6K","created_at":"2026-05-18T12:33:10Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:6IFNUX6KPOWTXYEWH5LIERLR3P","target":"record","payload":{"canonical_record":{"source":{"id":"1906.04818","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-06-11T20:58:25Z","cross_cats_sorted":["cs.LG","cs.NE"],"title_canon_sha256":"900599a30eb54567305495390e0c227f99845b29e7892af7c085cf957db087f1","abstract_canon_sha256":"ba246b55850e556d3c5826d75a927dfd734d2d6e517fe9e6bfd2416a44b3dc3e"},"schema_version":"1.0"},"canonical_sha256":"f20ada5fca7bad3be0963f56824571dbf395350e7a1dab1c57d616d820d43cc6","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:43:30.415726Z","signature_b64":"++17qeOd0FpKNOhQp6FQ5uwQbLUkhLOu1wIYo/K0F7NpgJ5KtL4b06NUqMhjqQ8eYxOXnDScRkfsLAnFJ6y8BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f20ada5fca7bad3be0963f56824571dbf395350e7a1dab1c57d616d820d43cc6","last_reissued_at":"2026-05-17T23:43:30.415325Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:43:30.415325Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1906.04818","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":"2026-05-17T23:43:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"uGuWF+hPTvsHM8s6sixgAJPpw02fDOM/4H7EB5GM8VMJDSMHiOxur0nzSS+8POr6Y0q+8IR0z9Ypa5T46gkmAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T19:49:27.419979Z"},"content_sha256":"be6573ace6ea40b4337596c25eee92e1f5943a6ae3f100ed3e7bc9cca7fe9e5e","schema_version":"1.0","event_id":"sha256:be6573ace6ea40b4337596c25eee92e1f5943a6ae3f100ed3e7bc9cca7fe9e5e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:6IFNUX6KPOWTXYEWH5LIERLR3P","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Medium-Term Load Forecasting Using Support Vector Regression, Feature Selection, and Symbiotic Organism Search Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.NE"],"primary_cat":"stat.ML","authors_text":"Arghavan Zare-Noghabi, Hossein Sangrody, Morteza Shabanzadeh","submitted_at":"2019-06-11T20:58:25Z","abstract_excerpt":"An accurate load forecasting has always been one of the main indispensable parts in the operation and planning of power systems. Among different time horizons of forecasting, while short-term load forecasting (STLF) and long-term load forecasting (LTLF) have respectively got benefits of accurate predictors and probabilistic forecasting, medium-term load forecasting (MTLF) demands more attention due to its vital role in power system operation and planning such as optimal scheduling of generation units, robust planning program for customer service, and economic supply. In this study, a hybrid me"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.04818","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":"2026-05-17T23:43:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZYpIq1YdXx2/ODaFCR4H3I5jBk8jT2rtM9oA+ji+aXqy0tq21+t7xCv3UvpxcVkHgmGZF/DFfvKfQghbugDmAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T19:49:27.420346Z"},"content_sha256":"d8f897277d14a9439fc4f38503c86308b6bc79721fd27a9cdb761bbb484b1f7c","schema_version":"1.0","event_id":"sha256:d8f897277d14a9439fc4f38503c86308b6bc79721fd27a9cdb761bbb484b1f7c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/6IFNUX6KPOWTXYEWH5LIERLR3P/bundle.json","state_url":"https://pith.science/pith/6IFNUX6KPOWTXYEWH5LIERLR3P/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/6IFNUX6KPOWTXYEWH5LIERLR3P/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":"2026-05-30T19:49:27Z","links":{"resolver":"https://pith.science/pith/6IFNUX6KPOWTXYEWH5LIERLR3P","bundle":"https://pith.science/pith/6IFNUX6KPOWTXYEWH5LIERLR3P/bundle.json","state":"https://pith.science/pith/6IFNUX6KPOWTXYEWH5LIERLR3P/state.json","well_known_bundle":"https://pith.science/.well-known/pith/6IFNUX6KPOWTXYEWH5LIERLR3P/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:6IFNUX6KPOWTXYEWH5LIERLR3P","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":"ba246b55850e556d3c5826d75a927dfd734d2d6e517fe9e6bfd2416a44b3dc3e","cross_cats_sorted":["cs.LG","cs.NE"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-06-11T20:58:25Z","title_canon_sha256":"900599a30eb54567305495390e0c227f99845b29e7892af7c085cf957db087f1"},"schema_version":"1.0","source":{"id":"1906.04818","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.04818","created_at":"2026-05-17T23:43:30Z"},{"alias_kind":"arxiv_version","alias_value":"1906.04818v1","created_at":"2026-05-17T23:43:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.04818","created_at":"2026-05-17T23:43:30Z"},{"alias_kind":"pith_short_12","alias_value":"6IFNUX6KPOWT","created_at":"2026-05-18T12:33:10Z"},{"alias_kind":"pith_short_16","alias_value":"6IFNUX6KPOWTXYEW","created_at":"2026-05-18T12:33:10Z"},{"alias_kind":"pith_short_8","alias_value":"6IFNUX6K","created_at":"2026-05-18T12:33:10Z"}],"graph_snapshots":[{"event_id":"sha256:d8f897277d14a9439fc4f38503c86308b6bc79721fd27a9cdb761bbb484b1f7c","target":"graph","created_at":"2026-05-17T23:43:30Z","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":"An accurate load forecasting has always been one of the main indispensable parts in the operation and planning of power systems. Among different time horizons of forecasting, while short-term load forecasting (STLF) and long-term load forecasting (LTLF) have respectively got benefits of accurate predictors and probabilistic forecasting, medium-term load forecasting (MTLF) demands more attention due to its vital role in power system operation and planning such as optimal scheduling of generation units, robust planning program for customer service, and economic supply. In this study, a hybrid me","authors_text":"Arghavan Zare-Noghabi, Hossein Sangrody, Morteza Shabanzadeh","cross_cats":["cs.LG","cs.NE"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-06-11T20:58:25Z","title":"Medium-Term Load Forecasting Using Support Vector Regression, Feature Selection, and Symbiotic Organism Search Optimization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.04818","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:be6573ace6ea40b4337596c25eee92e1f5943a6ae3f100ed3e7bc9cca7fe9e5e","target":"record","created_at":"2026-05-17T23:43:30Z","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":"ba246b55850e556d3c5826d75a927dfd734d2d6e517fe9e6bfd2416a44b3dc3e","cross_cats_sorted":["cs.LG","cs.NE"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-06-11T20:58:25Z","title_canon_sha256":"900599a30eb54567305495390e0c227f99845b29e7892af7c085cf957db087f1"},"schema_version":"1.0","source":{"id":"1906.04818","kind":"arxiv","version":1}},"canonical_sha256":"f20ada5fca7bad3be0963f56824571dbf395350e7a1dab1c57d616d820d43cc6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f20ada5fca7bad3be0963f56824571dbf395350e7a1dab1c57d616d820d43cc6","first_computed_at":"2026-05-17T23:43:30.415325Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:43:30.415325Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"++17qeOd0FpKNOhQp6FQ5uwQbLUkhLOu1wIYo/K0F7NpgJ5KtL4b06NUqMhjqQ8eYxOXnDScRkfsLAnFJ6y8BQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:43:30.415726Z","signed_message":"canonical_sha256_bytes"},"source_id":"1906.04818","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:be6573ace6ea40b4337596c25eee92e1f5943a6ae3f100ed3e7bc9cca7fe9e5e","sha256:d8f897277d14a9439fc4f38503c86308b6bc79721fd27a9cdb761bbb484b1f7c"],"state_sha256":"826a278cf6e9517daaea5e77fbb6332338fd07f75d773ae912050128ca2fb0cd"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"XrEBpDCqB6thrNuta0t2jjCYM77jN8FRnimKxjA2TJ2JfB4ySJQpq4YjmLfMCsZzLCERFA16JGHeZksYpidXDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-30T19:49:27.422622Z","bundle_sha256":"647636e99107af09256cd906594a2810663bcfdb14f5f16130d0842744ad93a6"}}