{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:T2WRGRJS3X6TB62V3RQZOYQ2GV","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":"eedca2bedde8eab72e2305066e4716881ecbefbb23437f26dc52d55904e0de83","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-10-25T07:47:56Z","title_canon_sha256":"1b29f1fd650a6907b2cd673ddc7b24e0e1d2cb7ee052c92bbbe79bf2fc9ec0d9"},"schema_version":"1.0","source":{"id":"1910.12618","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1910.12618","created_at":"2026-07-05T00:15:24Z"},{"alias_kind":"arxiv_version","alias_value":"1910.12618v2","created_at":"2026-07-05T00:15:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1910.12618","created_at":"2026-07-05T00:15:24Z"},{"alias_kind":"pith_short_12","alias_value":"T2WRGRJS3X6T","created_at":"2026-07-05T00:15:24Z"},{"alias_kind":"pith_short_16","alias_value":"T2WRGRJS3X6TB62V","created_at":"2026-07-05T00:15:24Z"},{"alias_kind":"pith_short_8","alias_value":"T2WRGRJS","created_at":"2026-07-05T00:15:24Z"}],"graph_snapshots":[{"event_id":"sha256:748563ef65ee450da426792ca090265f3dbd5d100ec0ca92510b7f4cbdc08616","target":"graph","created_at":"2026-07-05T00:15:24Z","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/1910.12618/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"While ubiquitous, textual sources of information such as company reports, social media posts, etc. are hardly included in prediction algorithms for time series, despite the relevant information they may contain. In this work, openly accessible daily weather reports from France and the United-Kingdom are leveraged to predict time series of national electricity consumption, average temperature and wind-speed with a single pipeline. Two methods of numerical representation of text are considered, namely traditional Term Frequency - Inverse Document Frequency (TF-IDF) as well as our own neural word","authors_text":"Badih Ghattas, David Obst, Georges Oppenheim, Jairo Cugliari, Sandra Claudel, Yannig Goude","cross_cats":["cs.LG","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-10-25T07:47:56Z","title":"Textual Data for Time Series Forecasting"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1910.12618","kind":"arxiv","version":2},"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":"sha256:414414ff9c5b0ed8d4d3cb5cf040b87a851f22ebe95a343a31ba058ec98848ae","target":"record","created_at":"2026-07-05T00:15:24Z","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":"eedca2bedde8eab72e2305066e4716881ecbefbb23437f26dc52d55904e0de83","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-10-25T07:47:56Z","title_canon_sha256":"1b29f1fd650a6907b2cd673ddc7b24e0e1d2cb7ee052c92bbbe79bf2fc9ec0d9"},"schema_version":"1.0","source":{"id":"1910.12618","kind":"arxiv","version":2}},"canonical_sha256":"9ead134532ddfd30fb55dc6197621a355e414bcd828092f3304a5f783cb6b9fe","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9ead134532ddfd30fb55dc6197621a355e414bcd828092f3304a5f783cb6b9fe","first_computed_at":"2026-07-05T00:15:24.833536Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:15:24.833536Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"bZkclNJXWYc6epoFWKh++wiWgUt+s+lH6oUaUbGp4jFAFsEEzSeMba+2RIE4Q8myj6a4ejVvaXmijS9qw/fuCA==","signature_status":"signed_v1","signed_at":"2026-07-05T00:15:24.834076Z","signed_message":"canonical_sha256_bytes"},"source_id":"1910.12618","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:414414ff9c5b0ed8d4d3cb5cf040b87a851f22ebe95a343a31ba058ec98848ae","sha256:748563ef65ee450da426792ca090265f3dbd5d100ec0ca92510b7f4cbdc08616"],"state_sha256":"e38985c015afdff078eea7804a079a835b36150763d9d6dff93150fcdac42e82"}