{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:3BTORUJ2E6DBOK6BPFT7XAS2C3","short_pith_number":"pith:3BTORUJ2","schema_version":"1.0","canonical_sha256":"d866e8d13a2786172bc17967fb825a16fd48082cdc8ae1e22ea49ec2e528247b","source":{"kind":"arxiv","id":"1906.04397","version":3},"attestation_state":"computed","paper":{"title":"Probabilistic Forecasting with Temporal Convolutional Neural Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Yanfei Kang, Yitian Chen, Yixiong Chen, Zizhuo Wang","submitted_at":"2019-06-11T05:26:11Z","abstract_excerpt":"We present a probabilistic forecasting framework based on convolutional neural network for multiple related time series forecasting. The framework can be applied to estimate probability density under both parametric and non-parametric settings. More specifically, stacked residual blocks based on dilated causal convolutional nets are constructed to capture the temporal dependencies of the series. Combined with representation learning, our approach is able to learn complex patterns such as seasonality, holiday effects within and across series, and to leverage those patterns for more accurate for"},"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":"1906.04397","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-06-11T05:26:11Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"b36ed36cb664b3d265199a6430ee3d8499a0bf024fdb3f3237899236466c9dd9","abstract_canon_sha256":"a5ea4c358be4023ce3a49f1b545e014febca66b2c1f957ede8e9eda73e6d110c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:47:57.743135Z","signature_b64":"LnEx6dO5zed/bYPwNc0yqYZwc+1Xkv+5MQi5p2nyTWVwkiZrFbLNywzX0J4Wp82zRARXmPF17bLnVGgMeeJwAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d866e8d13a2786172bc17967fb825a16fd48082cdc8ae1e22ea49ec2e528247b","last_reissued_at":"2026-07-05T00:47:57.742728Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:47:57.742728Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Probabilistic Forecasting with Temporal Convolutional Neural Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Yanfei Kang, Yitian Chen, Yixiong Chen, Zizhuo Wang","submitted_at":"2019-06-11T05:26:11Z","abstract_excerpt":"We present a probabilistic forecasting framework based on convolutional neural network for multiple related time series forecasting. The framework can be applied to estimate probability density under both parametric and non-parametric settings. More specifically, stacked residual blocks based on dilated causal convolutional nets are constructed to capture the temporal dependencies of the series. Combined with representation learning, our approach is able to learn complex patterns such as seasonality, holiday effects within and across series, and to leverage those patterns for more accurate for"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.04397","kind":"arxiv","version":3},"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/1906.04397/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":"1906.04397","created_at":"2026-07-05T00:47:57.742787+00:00"},{"alias_kind":"arxiv_version","alias_value":"1906.04397v3","created_at":"2026-07-05T00:47:57.742787+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.04397","created_at":"2026-07-05T00:47:57.742787+00:00"},{"alias_kind":"pith_short_12","alias_value":"3BTORUJ2E6DB","created_at":"2026-07-05T00:47:57.742787+00:00"},{"alias_kind":"pith_short_16","alias_value":"3BTORUJ2E6DBOK6B","created_at":"2026-07-05T00:47:57.742787+00:00"},{"alias_kind":"pith_short_8","alias_value":"3BTORUJ2","created_at":"2026-07-05T00:47:57.742787+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2508.18921","citing_title":"Forecasting Probability Distributions of Financial Returns with Deep Neural Networks","ref_index":2,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/3BTORUJ2E6DBOK6BPFT7XAS2C3","json":"https://pith.science/pith/3BTORUJ2E6DBOK6BPFT7XAS2C3.json","graph_json":"https://pith.science/api/pith-number/3BTORUJ2E6DBOK6BPFT7XAS2C3/graph.json","events_json":"https://pith.science/api/pith-number/3BTORUJ2E6DBOK6BPFT7XAS2C3/events.json","paper":"https://pith.science/paper/3BTORUJ2"},"agent_actions":{"view_html":"https://pith.science/pith/3BTORUJ2E6DBOK6BPFT7XAS2C3","download_json":"https://pith.science/pith/3BTORUJ2E6DBOK6BPFT7XAS2C3.json","view_paper":"https://pith.science/paper/3BTORUJ2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1906.04397&json=true","fetch_graph":"https://pith.science/api/pith-number/3BTORUJ2E6DBOK6BPFT7XAS2C3/graph.json","fetch_events":"https://pith.science/api/pith-number/3BTORUJ2E6DBOK6BPFT7XAS2C3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3BTORUJ2E6DBOK6BPFT7XAS2C3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3BTORUJ2E6DBOK6BPFT7XAS2C3/action/storage_attestation","attest_author":"https://pith.science/pith/3BTORUJ2E6DBOK6BPFT7XAS2C3/action/author_attestation","sign_citation":"https://pith.science/pith/3BTORUJ2E6DBOK6BPFT7XAS2C3/action/citation_signature","submit_replication":"https://pith.science/pith/3BTORUJ2E6DBOK6BPFT7XAS2C3/action/replication_record"}},"created_at":"2026-07-05T00:47:57.742787+00:00","updated_at":"2026-07-05T00:47:57.742787+00:00"}