{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:MS5FNYHBRVH6F55XDMGTWYCZ5L","short_pith_number":"pith:MS5FNYHB","schema_version":"1.0","canonical_sha256":"64ba56e0e18d4fe2f7b71b0d3b6059eace950e926eb654ca44e0f788aad771a5","source":{"kind":"arxiv","id":"2008.08878","version":1},"attestation_state":"computed","paper":{"title":"Reinforcement Learning based dynamic weighing of Ensemble Models for Time Series Forecasting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Bala Shyamala Balaji, Hemanth Kumar Tanneru, Satheesh K. Perepu, Sudhakar Kathari, Vivek Shankar Pinnamaraju","submitted_at":"2020-08-20T10:40:42Z","abstract_excerpt":"Ensemble models are powerful model building tools that are developed with a focus to improve the accuracy of model predictions. They find applications in time series forecasting in varied scenarios including but not limited to process industries, health care, and economics where a single model might not provide optimal performance. It is known that if models selected for data modelling are distinct (linear/non-linear, static/dynamic) and independent (minimally correlated models), the accuracy of the predictions is improved. Various approaches suggested in the literature to weigh the ensemble m"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2008.08878","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-08-20T10:40:42Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"a362608b6a522f69739f3c701b56ae83ee433fd9f854eb56cce6ada4e28ce7d6","abstract_canon_sha256":"4ac6b4991a3f87c4fe8c78b3b614a15a9f5dcabf8353cf221366b0c228e24df6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:28:37.339196Z","signature_b64":"Z5A3s0+11di+DtdXQ1pqw994khHnNvXojWTVX1UMvC+VWGz+iHvDW9IhbhU/mlvXAIzCbIwG42m+AM0nt8QOAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"64ba56e0e18d4fe2f7b71b0d3b6059eace950e926eb654ca44e0f788aad771a5","last_reissued_at":"2026-07-05T01:28:37.338773Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:28:37.338773Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Reinforcement Learning based dynamic weighing of Ensemble Models for Time Series Forecasting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Bala Shyamala Balaji, Hemanth Kumar Tanneru, Satheesh K. Perepu, Sudhakar Kathari, Vivek Shankar Pinnamaraju","submitted_at":"2020-08-20T10:40:42Z","abstract_excerpt":"Ensemble models are powerful model building tools that are developed with a focus to improve the accuracy of model predictions. They find applications in time series forecasting in varied scenarios including but not limited to process industries, health care, and economics where a single model might not provide optimal performance. It is known that if models selected for data modelling are distinct (linear/non-linear, static/dynamic) and independent (minimally correlated models), the accuracy of the predictions is improved. Various approaches suggested in the literature to weigh the ensemble m"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2008.08878","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/2008.08878/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2008.08878","created_at":"2026-07-05T01:28:37.338831+00:00"},{"alias_kind":"arxiv_version","alias_value":"2008.08878v1","created_at":"2026-07-05T01:28:37.338831+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2008.08878","created_at":"2026-07-05T01:28:37.338831+00:00"},{"alias_kind":"pith_short_12","alias_value":"MS5FNYHBRVH6","created_at":"2026-07-05T01:28:37.338831+00:00"},{"alias_kind":"pith_short_16","alias_value":"MS5FNYHBRVH6F55X","created_at":"2026-07-05T01:28:37.338831+00:00"},{"alias_kind":"pith_short_8","alias_value":"MS5FNYHB","created_at":"2026-07-05T01:28:37.338831+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2509.00687","citing_title":"Text Reinforcement for Multimodal Time Series Forecasting","ref_index":57,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MS5FNYHBRVH6F55XDMGTWYCZ5L","json":"https://pith.science/pith/MS5FNYHBRVH6F55XDMGTWYCZ5L.json","graph_json":"https://pith.science/api/pith-number/MS5FNYHBRVH6F55XDMGTWYCZ5L/graph.json","events_json":"https://pith.science/api/pith-number/MS5FNYHBRVH6F55XDMGTWYCZ5L/events.json","paper":"https://pith.science/paper/MS5FNYHB"},"agent_actions":{"view_html":"https://pith.science/pith/MS5FNYHBRVH6F55XDMGTWYCZ5L","download_json":"https://pith.science/pith/MS5FNYHBRVH6F55XDMGTWYCZ5L.json","view_paper":"https://pith.science/paper/MS5FNYHB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2008.08878&json=true","fetch_graph":"https://pith.science/api/pith-number/MS5FNYHBRVH6F55XDMGTWYCZ5L/graph.json","fetch_events":"https://pith.science/api/pith-number/MS5FNYHBRVH6F55XDMGTWYCZ5L/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MS5FNYHBRVH6F55XDMGTWYCZ5L/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MS5FNYHBRVH6F55XDMGTWYCZ5L/action/storage_attestation","attest_author":"https://pith.science/pith/MS5FNYHBRVH6F55XDMGTWYCZ5L/action/author_attestation","sign_citation":"https://pith.science/pith/MS5FNYHBRVH6F55XDMGTWYCZ5L/action/citation_signature","submit_replication":"https://pith.science/pith/MS5FNYHBRVH6F55XDMGTWYCZ5L/action/replication_record"}},"created_at":"2026-07-05T01:28:37.338831+00:00","updated_at":"2026-07-05T01:28:37.338831+00:00"}