{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:36TD23I6KCQ6AQFGFPORVM7VLU","short_pith_number":"pith:36TD23I6","schema_version":"1.0","canonical_sha256":"dfa63d6d1e50a1e040a62bdd1ab3f55d39c63a9a5e618b861d4203ba8e1d4287","source":{"kind":"arxiv","id":"1604.04044","version":1},"attestation_state":"computed","paper":{"title":"A Framework for Predictive Analysis of Stock Market Indices : A Study of the Indian Auto Sector","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.OH","authors_text":"Jaydip Sen, Tamal Datta Chaudhuri","submitted_at":"2016-04-14T06:39:31Z","abstract_excerpt":"Analysis and prediction of stock market time series data has attracted considerable interest from the research community over the last decade. Rapid development and evolution of sophisticated algorithms for statistical analysis of time series data, and availability of high-performance hardware has made it possible to process and analyze high volume stock market time series data effectively, in real-time. Among many other important characteristics and behavior of such data, forecasting is an area which has witnessed considerable focus. In this work, we have used time series of the index values "},"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":"1604.04044","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.OH","submitted_at":"2016-04-14T06:39:31Z","cross_cats_sorted":[],"title_canon_sha256":"48069db35980ee615c396dd6a1d147db180e746ae880920212481d8f6a044a67","abstract_canon_sha256":"888b66ea3fdc17c0c3970e7c094ba0883e03feef68e769b84d35203b568fb6bb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:15:13.542242Z","signature_b64":"GSNtBRfnKwunlYu3JJhDn36kxuls0MHZ+bqj94n+jAi5csd+YLKYE9cb5RReqs5JqvuuXe82eg83fsadzorDDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dfa63d6d1e50a1e040a62bdd1ab3f55d39c63a9a5e618b861d4203ba8e1d4287","last_reissued_at":"2026-05-18T01:15:13.541584Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:15:13.541584Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Framework for Predictive Analysis of Stock Market Indices : A Study of the Indian Auto Sector","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.OH","authors_text":"Jaydip Sen, Tamal Datta Chaudhuri","submitted_at":"2016-04-14T06:39:31Z","abstract_excerpt":"Analysis and prediction of stock market time series data has attracted considerable interest from the research community over the last decade. Rapid development and evolution of sophisticated algorithms for statistical analysis of time series data, and availability of high-performance hardware has made it possible to process and analyze high volume stock market time series data effectively, in real-time. Among many other important characteristics and behavior of such data, forecasting is an area which has witnessed considerable focus. In this work, we have used time series of the index values "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1604.04044","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":""},"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":"1604.04044","created_at":"2026-05-18T01:15:13.541685+00:00"},{"alias_kind":"arxiv_version","alias_value":"1604.04044v1","created_at":"2026-05-18T01:15:13.541685+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1604.04044","created_at":"2026-05-18T01:15:13.541685+00:00"},{"alias_kind":"pith_short_12","alias_value":"36TD23I6KCQ6","created_at":"2026-05-18T12:29:55.572404+00:00"},{"alias_kind":"pith_short_16","alias_value":"36TD23I6KCQ6AQFG","created_at":"2026-05-18T12:29:55.572404+00:00"},{"alias_kind":"pith_short_8","alias_value":"36TD23I6","created_at":"2026-05-18T12:29:55.572404+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/36TD23I6KCQ6AQFGFPORVM7VLU","json":"https://pith.science/pith/36TD23I6KCQ6AQFGFPORVM7VLU.json","graph_json":"https://pith.science/api/pith-number/36TD23I6KCQ6AQFGFPORVM7VLU/graph.json","events_json":"https://pith.science/api/pith-number/36TD23I6KCQ6AQFGFPORVM7VLU/events.json","paper":"https://pith.science/paper/36TD23I6"},"agent_actions":{"view_html":"https://pith.science/pith/36TD23I6KCQ6AQFGFPORVM7VLU","download_json":"https://pith.science/pith/36TD23I6KCQ6AQFGFPORVM7VLU.json","view_paper":"https://pith.science/paper/36TD23I6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1604.04044&json=true","fetch_graph":"https://pith.science/api/pith-number/36TD23I6KCQ6AQFGFPORVM7VLU/graph.json","fetch_events":"https://pith.science/api/pith-number/36TD23I6KCQ6AQFGFPORVM7VLU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/36TD23I6KCQ6AQFGFPORVM7VLU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/36TD23I6KCQ6AQFGFPORVM7VLU/action/storage_attestation","attest_author":"https://pith.science/pith/36TD23I6KCQ6AQFGFPORVM7VLU/action/author_attestation","sign_citation":"https://pith.science/pith/36TD23I6KCQ6AQFGFPORVM7VLU/action/citation_signature","submit_replication":"https://pith.science/pith/36TD23I6KCQ6AQFGFPORVM7VLU/action/replication_record"}},"created_at":"2026-05-18T01:15:13.541685+00:00","updated_at":"2026-05-18T01:15:13.541685+00:00"}