{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:ANICC4YCK73L5AG57NJQ6SUT6U","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":"9742deaa14f685223152471904d992eca77d57918efc6594d58687113263a007","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-04-21T09:40:30Z","title_canon_sha256":"7f63341c057fb72b83b3591ee49adf7d2bff93179c560dea85dc526b56f8cdc7"},"schema_version":"1.0","source":{"id":"2204.09994","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2204.09994","created_at":"2026-07-05T04:16:45Z"},{"alias_kind":"arxiv_version","alias_value":"2204.09994v1","created_at":"2026-07-05T04:16:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2204.09994","created_at":"2026-07-05T04:16:45Z"},{"alias_kind":"pith_short_12","alias_value":"ANICC4YCK73L","created_at":"2026-07-05T04:16:45Z"},{"alias_kind":"pith_short_16","alias_value":"ANICC4YCK73L5AG5","created_at":"2026-07-05T04:16:45Z"},{"alias_kind":"pith_short_8","alias_value":"ANICC4YC","created_at":"2026-07-05T04:16:45Z"}],"graph_snapshots":[{"event_id":"sha256:256c5d5cf3774e5ee375e0c88037973b435f9eb4c4c6724197d4ab0f5a7284e1","target":"graph","created_at":"2026-07-05T04:16:45Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2204.09994/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In the process of collecting data from sensors, several circumstances can affect their continuity and validity, resulting in alterations of the data or loss of information. Although classical methods of statistics, such as interpolation-like techniques, can be used to approximate the missing data in a time series, the recent developments in Deep Learning (DL) have given impetus to innovative and much more accurate forecasting techniques. In the present paper, we develop two DL models aimed at filling data gaps, for the specific case of internal temperature time series obtained from monitored a","authors_text":"Aaron Estrada (1), Bologna, Bolzano, Italy), Italy (2) Centro Euro-Mediterraneo sui Cambiamenti Climatici, Kostas Tzoumpas (1), Pietro Miraglio (2), Pietro Zambelli (1) ((1) Eurac Research - Institute for Renewable Energy","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-04-21T09:40:30Z","title":"A data filling methodology for time series based on CNN and (Bi)LSTM neural networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2204.09994","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":"sha256:3d7ff4a55a6edad11b1916dc1162c9957501f8da2891b73409eeeb0be4a0da0d","target":"record","created_at":"2026-07-05T04:16:45Z","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":"9742deaa14f685223152471904d992eca77d57918efc6594d58687113263a007","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-04-21T09:40:30Z","title_canon_sha256":"7f63341c057fb72b83b3591ee49adf7d2bff93179c560dea85dc526b56f8cdc7"},"schema_version":"1.0","source":{"id":"2204.09994","kind":"arxiv","version":1}},"canonical_sha256":"035021730257f6be80ddfb530f4a93f503302342958de4ec4f78e9a62359ea31","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"035021730257f6be80ddfb530f4a93f503302342958de4ec4f78e9a62359ea31","first_computed_at":"2026-07-05T04:16:45.717802Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:16:45.717802Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"LWHGPIIY2I5K6LDnbCRn7jIm1X7ohMHOeT9ap98BYOtNjkXwGK2zWMXFCFepDlgbtPrchAox4K0uVlaA+ByFCw==","signature_status":"signed_v1","signed_at":"2026-07-05T04:16:45.721819Z","signed_message":"canonical_sha256_bytes"},"source_id":"2204.09994","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3d7ff4a55a6edad11b1916dc1162c9957501f8da2891b73409eeeb0be4a0da0d","sha256:256c5d5cf3774e5ee375e0c88037973b435f9eb4c4c6724197d4ab0f5a7284e1"],"state_sha256":"4ca9140e0c457d490fb9b176d95b8946e00f86a24665a0ae054080a10dff6e73"}