{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:MATKAKKBMBCVHMHD5DOXS7KOYS","short_pith_number":"pith:MATKAKKB","schema_version":"1.0","canonical_sha256":"6026a02941604553b0e3e8dd797d4ec487388f04d2fa0b2db693103d88ed20c8","source":{"kind":"arxiv","id":"2001.01553","version":1},"attestation_state":"computed","paper":{"title":"DeepAuto: A Hierarchical Deep Learning Framework for Real-Time Prediction in Cellular Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NI"],"primary_cat":"eess.SP","authors_text":"Abhijeet Bhorkar, Jin Wang, Ke Zhang","submitted_at":"2019-12-12T03:36:57Z","abstract_excerpt":"Accurate real-time forecasting of key performance indicators (KPIs) is an essential requirement for various LTE/5G radio access network (RAN) automation. However, an accurate prediction can be very challenging in large-scale cellular environments due to complex spatio-temporal dynamics, network configuration changes and unavailability of real-time network data. In this work, we introduce a reusable analytics framework that enables real-time KPI prediction using a hierarchical deep learning architecture. Our prediction approach, namely DeepAuto, stacks multiple long short-term memory (LSTM) net"},"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":"2001.01553","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2019-12-12T03:36:57Z","cross_cats_sorted":["cs.NI"],"title_canon_sha256":"2ee46537274511cdeac280a4119e518bd7dcebf5a0be9b8d3a7901d285c75b85","abstract_canon_sha256":"b4d03676d5a85d1c1619674753eb89cce20e2b183ced096d74a44eabe3def3c0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:29:47.762388Z","signature_b64":"M50tp484cL2u9SVqatFYekXMv7n+etczQuSMkmlTCkBpXUFHiEeZGWi320il6xZkHPUSFmQO0Ps6Vmpc+Av2BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6026a02941604553b0e3e8dd797d4ec487388f04d2fa0b2db693103d88ed20c8","last_reissued_at":"2026-07-05T00:29:47.761956Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:29:47.761956Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DeepAuto: A Hierarchical Deep Learning Framework for Real-Time Prediction in Cellular Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NI"],"primary_cat":"eess.SP","authors_text":"Abhijeet Bhorkar, Jin Wang, Ke Zhang","submitted_at":"2019-12-12T03:36:57Z","abstract_excerpt":"Accurate real-time forecasting of key performance indicators (KPIs) is an essential requirement for various LTE/5G radio access network (RAN) automation. However, an accurate prediction can be very challenging in large-scale cellular environments due to complex spatio-temporal dynamics, network configuration changes and unavailability of real-time network data. In this work, we introduce a reusable analytics framework that enables real-time KPI prediction using a hierarchical deep learning architecture. Our prediction approach, namely DeepAuto, stacks multiple long short-term memory (LSTM) net"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2001.01553","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/2001.01553/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":"2001.01553","created_at":"2026-07-05T00:29:47.762017+00:00"},{"alias_kind":"arxiv_version","alias_value":"2001.01553v1","created_at":"2026-07-05T00:29:47.762017+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2001.01553","created_at":"2026-07-05T00:29:47.762017+00:00"},{"alias_kind":"pith_short_12","alias_value":"MATKAKKBMBCV","created_at":"2026-07-05T00:29:47.762017+00:00"},{"alias_kind":"pith_short_16","alias_value":"MATKAKKBMBCVHMHD","created_at":"2026-07-05T00:29:47.762017+00:00"},{"alias_kind":"pith_short_8","alias_value":"MATKAKKB","created_at":"2026-07-05T00:29:47.762017+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.15688","citing_title":"Cellular Traffic Prediction via Deep State Space Models with Attention Mechanism","ref_index":51,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MATKAKKBMBCVHMHD5DOXS7KOYS","json":"https://pith.science/pith/MATKAKKBMBCVHMHD5DOXS7KOYS.json","graph_json":"https://pith.science/api/pith-number/MATKAKKBMBCVHMHD5DOXS7KOYS/graph.json","events_json":"https://pith.science/api/pith-number/MATKAKKBMBCVHMHD5DOXS7KOYS/events.json","paper":"https://pith.science/paper/MATKAKKB"},"agent_actions":{"view_html":"https://pith.science/pith/MATKAKKBMBCVHMHD5DOXS7KOYS","download_json":"https://pith.science/pith/MATKAKKBMBCVHMHD5DOXS7KOYS.json","view_paper":"https://pith.science/paper/MATKAKKB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2001.01553&json=true","fetch_graph":"https://pith.science/api/pith-number/MATKAKKBMBCVHMHD5DOXS7KOYS/graph.json","fetch_events":"https://pith.science/api/pith-number/MATKAKKBMBCVHMHD5DOXS7KOYS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MATKAKKBMBCVHMHD5DOXS7KOYS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MATKAKKBMBCVHMHD5DOXS7KOYS/action/storage_attestation","attest_author":"https://pith.science/pith/MATKAKKBMBCVHMHD5DOXS7KOYS/action/author_attestation","sign_citation":"https://pith.science/pith/MATKAKKBMBCVHMHD5DOXS7KOYS/action/citation_signature","submit_replication":"https://pith.science/pith/MATKAKKBMBCVHMHD5DOXS7KOYS/action/replication_record"}},"created_at":"2026-07-05T00:29:47.762017+00:00","updated_at":"2026-07-05T00:29:47.762017+00:00"}