{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:QNVIEMWJIIN6EWSG2KVIXB4KSZ","short_pith_number":"pith:QNVIEMWJ","schema_version":"1.0","canonical_sha256":"836a8232c9421be25a46d2aa8b878a967e0cc8c6f583de3877136010457b505d","source":{"kind":"arxiv","id":"2009.14379","version":1},"attestation_state":"computed","paper":{"title":"Few-shot Learning for Time-series Forecasting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Atsutoshi Kumagai, Tomoharu Iwata","submitted_at":"2020-09-30T01:32:22Z","abstract_excerpt":"Time-series forecasting is important for many applications. Forecasting models are usually trained using time-series data in a specific target task. However, sufficient data in the target task might be unavailable, which leads to performance degradation. In this paper, we propose a few-shot learning method that forecasts a future value of a time-series in a target task given a few time-series in the target task. Our model is trained using time-series data in multiple training tasks that are different from target tasks. Our model uses a few time-series to build a forecasting function based on a"},"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":"2009.14379","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2020-09-30T01:32:22Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"2086b07a65193ef6159441dedaafa047509934c44d4ba6edf34082b8cf581754","abstract_canon_sha256":"462111e125ce38f51e49db399626c2700c7eb605d0d1fe6ec1380bb05547337e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:39:18.826496Z","signature_b64":"xC0ZqIQOQL0J4OJlVd36qw4l7uPYatqoIvTJKUblFNwFxQoBnRYi/3RZpai3BBpYZx41JeAFxzHaOrNAWDAUAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"836a8232c9421be25a46d2aa8b878a967e0cc8c6f583de3877136010457b505d","last_reissued_at":"2026-07-05T01:39:18.826074Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:39:18.826074Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Few-shot Learning for Time-series Forecasting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Atsutoshi Kumagai, Tomoharu Iwata","submitted_at":"2020-09-30T01:32:22Z","abstract_excerpt":"Time-series forecasting is important for many applications. Forecasting models are usually trained using time-series data in a specific target task. However, sufficient data in the target task might be unavailable, which leads to performance degradation. In this paper, we propose a few-shot learning method that forecasts a future value of a time-series in a target task given a few time-series in the target task. Our model is trained using time-series data in multiple training tasks that are different from target tasks. Our model uses a few time-series to build a forecasting function based on a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2009.14379","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/2009.14379/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":"2009.14379","created_at":"2026-07-05T01:39:18.826133+00:00"},{"alias_kind":"arxiv_version","alias_value":"2009.14379v1","created_at":"2026-07-05T01:39:18.826133+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2009.14379","created_at":"2026-07-05T01:39:18.826133+00:00"},{"alias_kind":"pith_short_12","alias_value":"QNVIEMWJIIN6","created_at":"2026-07-05T01:39:18.826133+00:00"},{"alias_kind":"pith_short_16","alias_value":"QNVIEMWJIIN6EWSG","created_at":"2026-07-05T01:39:18.826133+00:00"},{"alias_kind":"pith_short_8","alias_value":"QNVIEMWJ","created_at":"2026-07-05T01:39:18.826133+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2402.13228","citing_title":"Smaug: Fixing Failure Modes of Preference Optimisation with DPO-Positive","ref_index":144,"is_internal_anchor":false},{"citing_arxiv_id":"2604.26762","citing_title":"Exploring the Potential of Probabilistic Transformer for Time Series Modeling: A Report on the ST-PT Framework","ref_index":32,"is_internal_anchor":false},{"citing_arxiv_id":"2604.05543","citing_title":"Channel-wise Retrieval for Multivariate Time Series Forecasting","ref_index":24,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QNVIEMWJIIN6EWSG2KVIXB4KSZ","json":"https://pith.science/pith/QNVIEMWJIIN6EWSG2KVIXB4KSZ.json","graph_json":"https://pith.science/api/pith-number/QNVIEMWJIIN6EWSG2KVIXB4KSZ/graph.json","events_json":"https://pith.science/api/pith-number/QNVIEMWJIIN6EWSG2KVIXB4KSZ/events.json","paper":"https://pith.science/paper/QNVIEMWJ"},"agent_actions":{"view_html":"https://pith.science/pith/QNVIEMWJIIN6EWSG2KVIXB4KSZ","download_json":"https://pith.science/pith/QNVIEMWJIIN6EWSG2KVIXB4KSZ.json","view_paper":"https://pith.science/paper/QNVIEMWJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2009.14379&json=true","fetch_graph":"https://pith.science/api/pith-number/QNVIEMWJIIN6EWSG2KVIXB4KSZ/graph.json","fetch_events":"https://pith.science/api/pith-number/QNVIEMWJIIN6EWSG2KVIXB4KSZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QNVIEMWJIIN6EWSG2KVIXB4KSZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QNVIEMWJIIN6EWSG2KVIXB4KSZ/action/storage_attestation","attest_author":"https://pith.science/pith/QNVIEMWJIIN6EWSG2KVIXB4KSZ/action/author_attestation","sign_citation":"https://pith.science/pith/QNVIEMWJIIN6EWSG2KVIXB4KSZ/action/citation_signature","submit_replication":"https://pith.science/pith/QNVIEMWJIIN6EWSG2KVIXB4KSZ/action/replication_record"}},"created_at":"2026-07-05T01:39:18.826133+00:00","updated_at":"2026-07-05T01:39:18.826133+00:00"}