{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:32LEXJROO6QDXV75HGXE5B7SQ2","short_pith_number":"pith:32LEXJRO","schema_version":"1.0","canonical_sha256":"de964ba62e77a03bd7fd39ae4e87f2868b6fee17f1b506359c481423c1209518","source":{"kind":"arxiv","id":"2002.05784","version":1},"attestation_state":"computed","paper":{"title":"Improving S&P stock prediction with time series stock similarity","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"q-fin.ST","authors_text":"Lior Sidi","submitted_at":"2020-02-08T14:13:45Z","abstract_excerpt":"Stock market prediction with forecasting algorithms is a popular topic these days where most of the forecasting algorithms train only on data collected on a particular stock. In this paper, we enriched the stock data with related stocks just as a professional trader would have done to improve the stock prediction models. We tested five different similarities functions and found co-integration similarity to have the best improvement on the prediction model. We evaluate the models on seven S&P stocks from various industries over five years period. The prediction model we trained on similar stock"},"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":"2002.05784","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"q-fin.ST","submitted_at":"2020-02-08T14:13:45Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"d0f7af19f251aa193b14cc1238fa4c6f70380179281a71b088ab4404b15817c5","abstract_canon_sha256":"1514f58970dc4b60f6ca4746bda7f9ded04904854fb1a9fe43645753748e077f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:40:35.942513Z","signature_b64":"UAzrIXbHD2elC/t+KjTd1uMpNV/6z1Y/PczrAubXFWHTSw3VjSc0CF/ZCAcmVidR8MMWcKmuM8V5xYPiWYs5Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"de964ba62e77a03bd7fd39ae4e87f2868b6fee17f1b506359c481423c1209518","last_reissued_at":"2026-07-05T00:40:35.942060Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:40:35.942060Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Improving S&P stock prediction with time series stock similarity","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"q-fin.ST","authors_text":"Lior Sidi","submitted_at":"2020-02-08T14:13:45Z","abstract_excerpt":"Stock market prediction with forecasting algorithms is a popular topic these days where most of the forecasting algorithms train only on data collected on a particular stock. In this paper, we enriched the stock data with related stocks just as a professional trader would have done to improve the stock prediction models. We tested five different similarities functions and found co-integration similarity to have the best improvement on the prediction model. We evaluate the models on seven S&P stocks from various industries over five years period. The prediction model we trained on similar stock"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2002.05784","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/2002.05784/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":"2002.05784","created_at":"2026-07-05T00:40:35.942126+00:00"},{"alias_kind":"arxiv_version","alias_value":"2002.05784v1","created_at":"2026-07-05T00:40:35.942126+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2002.05784","created_at":"2026-07-05T00:40:35.942126+00:00"},{"alias_kind":"pith_short_12","alias_value":"32LEXJROO6QD","created_at":"2026-07-05T00:40:35.942126+00:00"},{"alias_kind":"pith_short_16","alias_value":"32LEXJROO6QDXV75","created_at":"2026-07-05T00:40:35.942126+00:00"},{"alias_kind":"pith_short_8","alias_value":"32LEXJRO","created_at":"2026-07-05T00:40:35.942126+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.06504","citing_title":"RMISC: A Large-scale Real-world Multivariate Corpus for Time Series Foundation Models","ref_index":43,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/32LEXJROO6QDXV75HGXE5B7SQ2","json":"https://pith.science/pith/32LEXJROO6QDXV75HGXE5B7SQ2.json","graph_json":"https://pith.science/api/pith-number/32LEXJROO6QDXV75HGXE5B7SQ2/graph.json","events_json":"https://pith.science/api/pith-number/32LEXJROO6QDXV75HGXE5B7SQ2/events.json","paper":"https://pith.science/paper/32LEXJRO"},"agent_actions":{"view_html":"https://pith.science/pith/32LEXJROO6QDXV75HGXE5B7SQ2","download_json":"https://pith.science/pith/32LEXJROO6QDXV75HGXE5B7SQ2.json","view_paper":"https://pith.science/paper/32LEXJRO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2002.05784&json=true","fetch_graph":"https://pith.science/api/pith-number/32LEXJROO6QDXV75HGXE5B7SQ2/graph.json","fetch_events":"https://pith.science/api/pith-number/32LEXJROO6QDXV75HGXE5B7SQ2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/32LEXJROO6QDXV75HGXE5B7SQ2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/32LEXJROO6QDXV75HGXE5B7SQ2/action/storage_attestation","attest_author":"https://pith.science/pith/32LEXJROO6QDXV75HGXE5B7SQ2/action/author_attestation","sign_citation":"https://pith.science/pith/32LEXJROO6QDXV75HGXE5B7SQ2/action/citation_signature","submit_replication":"https://pith.science/pith/32LEXJROO6QDXV75HGXE5B7SQ2/action/replication_record"}},"created_at":"2026-07-05T00:40:35.942126+00:00","updated_at":"2026-07-05T00:40:35.942126+00:00"}