{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:655GGDHBALLR7ZATX3ULKI6CB4","short_pith_number":"pith:655GGDHB","canonical_record":{"source":{"id":"1706.09648","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2017-06-29T09:36:45Z","cross_cats_sorted":[],"title_canon_sha256":"b2d89fb62a39dcd2f8976f02d2f93278efddc4de423a54d5be6bcbb0ca547a95","abstract_canon_sha256":"634b81902632974e8e19b084aa7444daec2e131461bcaaabe251c9998dbda46f"},"schema_version":"1.0"},"canonical_sha256":"f77a630ce102d71fe413bee8b523c20f29a27b1d5fbc1834e894d69ca3762645","source":{"kind":"arxiv","id":"1706.09648","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1706.09648","created_at":"2026-05-18T00:41:14Z"},{"alias_kind":"arxiv_version","alias_value":"1706.09648v1","created_at":"2026-05-18T00:41:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.09648","created_at":"2026-05-18T00:41:14Z"},{"alias_kind":"pith_short_12","alias_value":"655GGDHBALLR","created_at":"2026-05-18T12:31:03Z"},{"alias_kind":"pith_short_16","alias_value":"655GGDHBALLR7ZAT","created_at":"2026-05-18T12:31:03Z"},{"alias_kind":"pith_short_8","alias_value":"655GGDHB","created_at":"2026-05-18T12:31:03Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:655GGDHBALLR7ZATX3ULKI6CB4","target":"record","payload":{"canonical_record":{"source":{"id":"1706.09648","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2017-06-29T09:36:45Z","cross_cats_sorted":[],"title_canon_sha256":"b2d89fb62a39dcd2f8976f02d2f93278efddc4de423a54d5be6bcbb0ca547a95","abstract_canon_sha256":"634b81902632974e8e19b084aa7444daec2e131461bcaaabe251c9998dbda46f"},"schema_version":"1.0"},"canonical_sha256":"f77a630ce102d71fe413bee8b523c20f29a27b1d5fbc1834e894d69ca3762645","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:41:14.272236Z","signature_b64":"UXVzRkJnT+T2mnnGE0m+1ecOCWWETen1QEiAndsdiIFpNoUaPf+B6EwwBQtD0cRcUlsRjKTXSM4AT3CeFCyWDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f77a630ce102d71fe413bee8b523c20f29a27b1d5fbc1834e894d69ca3762645","last_reissued_at":"2026-05-18T00:41:14.271531Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:41:14.271531Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1706.09648","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":"2026-05-18T00:41:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4NcmNuIwxwIHIt/lZYu+5gQk39zXX2wrAu0hihAiFDdBkowLHCsvgd77dFSsbZ1ZjJ8W0XQZzuoH3Pd83Ed8BA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T23:29:29.698959Z"},"content_sha256":"97d25f4c303370d6cee0e2b503b3f70eab87e570bb78ce7c9d64f747ffe4d7a7","schema_version":"1.0","event_id":"sha256:97d25f4c303370d6cee0e2b503b3f70eab87e570bb78ce7c9d64f747ffe4d7a7"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:655GGDHBALLR7ZATX3ULKI6CB4","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Machine Learning Approaches to Energy Consumption Forecasting in Households","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.NE","authors_text":"Michele Rossi, Riccardo Bonetto","submitted_at":"2017-06-29T09:36:45Z","abstract_excerpt":"We consider the problem of power demand forecasting in residential micro-grids. Several approaches using ARMA models, support vector machines, and recurrent neural networks that perform one-step ahead predictions have been proposed in the literature. Here, we extend them to perform multi-step ahead forecasting and we compare their performance. Toward this end, we implement a parallel and efficient training framework, using power demand traces from real deployments to gauge the accuracy of the considered techniques. Our results indicate that machine learning schemes achieve smaller prediction e"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.09648","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":"2026-05-18T00:41:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mPXZSq98mbiSfXK+MVboRPoMFD7WjjKDU+R+KpQ78wJKCbqhyGNdDmH3wHKFy1ZzUypYdMnAdC8B+rNMjZbJAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T23:29:29.699533Z"},"content_sha256":"c4901b77232b1e8e30fa521c665225bff5ffdea613739d60ee6acf9c6e8cdc0b","schema_version":"1.0","event_id":"sha256:c4901b77232b1e8e30fa521c665225bff5ffdea613739d60ee6acf9c6e8cdc0b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/655GGDHBALLR7ZATX3ULKI6CB4/bundle.json","state_url":"https://pith.science/pith/655GGDHBALLR7ZATX3ULKI6CB4/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/655GGDHBALLR7ZATX3ULKI6CB4/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":"2026-05-25T23:29:29Z","links":{"resolver":"https://pith.science/pith/655GGDHBALLR7ZATX3ULKI6CB4","bundle":"https://pith.science/pith/655GGDHBALLR7ZATX3ULKI6CB4/bundle.json","state":"https://pith.science/pith/655GGDHBALLR7ZATX3ULKI6CB4/state.json","well_known_bundle":"https://pith.science/.well-known/pith/655GGDHBALLR7ZATX3ULKI6CB4/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:655GGDHBALLR7ZATX3ULKI6CB4","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":"634b81902632974e8e19b084aa7444daec2e131461bcaaabe251c9998dbda46f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2017-06-29T09:36:45Z","title_canon_sha256":"b2d89fb62a39dcd2f8976f02d2f93278efddc4de423a54d5be6bcbb0ca547a95"},"schema_version":"1.0","source":{"id":"1706.09648","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1706.09648","created_at":"2026-05-18T00:41:14Z"},{"alias_kind":"arxiv_version","alias_value":"1706.09648v1","created_at":"2026-05-18T00:41:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.09648","created_at":"2026-05-18T00:41:14Z"},{"alias_kind":"pith_short_12","alias_value":"655GGDHBALLR","created_at":"2026-05-18T12:31:03Z"},{"alias_kind":"pith_short_16","alias_value":"655GGDHBALLR7ZAT","created_at":"2026-05-18T12:31:03Z"},{"alias_kind":"pith_short_8","alias_value":"655GGDHB","created_at":"2026-05-18T12:31:03Z"}],"graph_snapshots":[{"event_id":"sha256:c4901b77232b1e8e30fa521c665225bff5ffdea613739d60ee6acf9c6e8cdc0b","target":"graph","created_at":"2026-05-18T00:41:14Z","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":"We consider the problem of power demand forecasting in residential micro-grids. Several approaches using ARMA models, support vector machines, and recurrent neural networks that perform one-step ahead predictions have been proposed in the literature. Here, we extend them to perform multi-step ahead forecasting and we compare their performance. Toward this end, we implement a parallel and efficient training framework, using power demand traces from real deployments to gauge the accuracy of the considered techniques. Our results indicate that machine learning schemes achieve smaller prediction e","authors_text":"Michele Rossi, Riccardo Bonetto","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2017-06-29T09:36:45Z","title":"Machine Learning Approaches to Energy Consumption Forecasting in Households"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.09648","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":"sha256:97d25f4c303370d6cee0e2b503b3f70eab87e570bb78ce7c9d64f747ffe4d7a7","target":"record","created_at":"2026-05-18T00:41:14Z","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":"634b81902632974e8e19b084aa7444daec2e131461bcaaabe251c9998dbda46f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2017-06-29T09:36:45Z","title_canon_sha256":"b2d89fb62a39dcd2f8976f02d2f93278efddc4de423a54d5be6bcbb0ca547a95"},"schema_version":"1.0","source":{"id":"1706.09648","kind":"arxiv","version":1}},"canonical_sha256":"f77a630ce102d71fe413bee8b523c20f29a27b1d5fbc1834e894d69ca3762645","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f77a630ce102d71fe413bee8b523c20f29a27b1d5fbc1834e894d69ca3762645","first_computed_at":"2026-05-18T00:41:14.271531Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:41:14.271531Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"UXVzRkJnT+T2mnnGE0m+1ecOCWWETen1QEiAndsdiIFpNoUaPf+B6EwwBQtD0cRcUlsRjKTXSM4AT3CeFCyWDw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:41:14.272236Z","signed_message":"canonical_sha256_bytes"},"source_id":"1706.09648","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:97d25f4c303370d6cee0e2b503b3f70eab87e570bb78ce7c9d64f747ffe4d7a7","sha256:c4901b77232b1e8e30fa521c665225bff5ffdea613739d60ee6acf9c6e8cdc0b"],"state_sha256":"86f5b66320413d2d9c1c3303b6730b4599779491546452be1d77cf5c1ed81f7b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"XtwAWXCFf8XEHjiVQXYLDlNE5i/AD+lNfTdnqJ6UBRSJXMhHff15dB7Pf30eTysJpmprqtXWYI/sduNKPq3oDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T23:29:29.702941Z","bundle_sha256":"55b26e11e9375ff57bba0ff3fef1a62adbca20817905e833fc4cda34c31203e3"}}