{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:E2DRMARXZQLBKO66BAJGDQ5ZKR","short_pith_number":"pith:E2DRMARX","schema_version":"1.0","canonical_sha256":"2687160237cc16153bde081261c3b954679f6a83c71984b5b3c43193e758245d","source":{"kind":"arxiv","id":"2408.17253","version":4},"attestation_state":"computed","paper":{"title":"VisionTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasters","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Chenghao Liu, Jianling Sun, Lefei Shen, Mouxiang Chen, Xiaoyun Joy Wang, Zhuo Li","submitted_at":"2024-08-30T12:51:55Z","abstract_excerpt":"Foundation models have emerged as a promising approach in time series forecasting (TSF). Existing approaches either repurpose large language models (LLMs) or build large-scale time series datasets to develop TSF foundation models for universal forecasting. However, these methods face challenges due to the severe cross-domain gap or in-domain heterogeneity. This paper explores a new road to building a TSF foundation model from rich, high-quality natural images. Our key insight is that a visual masked autoencoder, pre-trained on the ImageNet dataset, can naturally be a numeric series forecaster."},"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":"2408.17253","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-08-30T12:51:55Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"df8776b69bc8b23fbf982201393d08b91b7a76c0f554d3c655194de885741a55","abstract_canon_sha256":"cfab5137897ec383d01199679ce608625c0a1e5a01dd9f9dd1f8332bf2945796"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:17:22.324296Z","signature_b64":"dpXjr8P2kqZwux1w/Ygto51VDZ8ymeqv97A0GmKM+92Qtb+F9LUek/wBYdwOAs1+qkRsq6Rx3VAVDXlwKUL/Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2687160237cc16153bde081261c3b954679f6a83c71984b5b3c43193e758245d","last_reissued_at":"2026-07-05T11:17:22.323774Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:17:22.323774Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"VisionTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasters","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Chenghao Liu, Jianling Sun, Lefei Shen, Mouxiang Chen, Xiaoyun Joy Wang, Zhuo Li","submitted_at":"2024-08-30T12:51:55Z","abstract_excerpt":"Foundation models have emerged as a promising approach in time series forecasting (TSF). Existing approaches either repurpose large language models (LLMs) or build large-scale time series datasets to develop TSF foundation models for universal forecasting. However, these methods face challenges due to the severe cross-domain gap or in-domain heterogeneity. This paper explores a new road to building a TSF foundation model from rich, high-quality natural images. Our key insight is that a visual masked autoencoder, pre-trained on the ImageNet dataset, can naturally be a numeric series forecaster."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.17253","kind":"arxiv","version":4},"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/2408.17253/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":"2408.17253","created_at":"2026-07-05T11:17:22.323842+00:00"},{"alias_kind":"arxiv_version","alias_value":"2408.17253v4","created_at":"2026-07-05T11:17:22.323842+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.17253","created_at":"2026-07-05T11:17:22.323842+00:00"},{"alias_kind":"pith_short_12","alias_value":"E2DRMARXZQLB","created_at":"2026-07-05T11:17:22.323842+00:00"},{"alias_kind":"pith_short_16","alias_value":"E2DRMARXZQLBKO66","created_at":"2026-07-05T11:17:22.323842+00:00"},{"alias_kind":"pith_short_8","alias_value":"E2DRMARX","created_at":"2026-07-05T11:17:22.323842+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2607.01918","citing_title":"Zeus: Towards Tuning-Free Foundation Model for Time Series Analysis","ref_index":175,"is_internal_anchor":false},{"citing_arxiv_id":"2509.15105","citing_title":"Super-Linear: A Lightweight Pretrained Mixture of Linear Experts for Time Series Forecasting","ref_index":8,"is_internal_anchor":false},{"citing_arxiv_id":"2506.11512","citing_title":"From Time Series Analysis to Question Answering: A Survey in the LLM Era","ref_index":14,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/E2DRMARXZQLBKO66BAJGDQ5ZKR","json":"https://pith.science/pith/E2DRMARXZQLBKO66BAJGDQ5ZKR.json","graph_json":"https://pith.science/api/pith-number/E2DRMARXZQLBKO66BAJGDQ5ZKR/graph.json","events_json":"https://pith.science/api/pith-number/E2DRMARXZQLBKO66BAJGDQ5ZKR/events.json","paper":"https://pith.science/paper/E2DRMARX"},"agent_actions":{"view_html":"https://pith.science/pith/E2DRMARXZQLBKO66BAJGDQ5ZKR","download_json":"https://pith.science/pith/E2DRMARXZQLBKO66BAJGDQ5ZKR.json","view_paper":"https://pith.science/paper/E2DRMARX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2408.17253&json=true","fetch_graph":"https://pith.science/api/pith-number/E2DRMARXZQLBKO66BAJGDQ5ZKR/graph.json","fetch_events":"https://pith.science/api/pith-number/E2DRMARXZQLBKO66BAJGDQ5ZKR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/E2DRMARXZQLBKO66BAJGDQ5ZKR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/E2DRMARXZQLBKO66BAJGDQ5ZKR/action/storage_attestation","attest_author":"https://pith.science/pith/E2DRMARXZQLBKO66BAJGDQ5ZKR/action/author_attestation","sign_citation":"https://pith.science/pith/E2DRMARXZQLBKO66BAJGDQ5ZKR/action/citation_signature","submit_replication":"https://pith.science/pith/E2DRMARXZQLBKO66BAJGDQ5ZKR/action/replication_record"}},"created_at":"2026-07-05T11:17:22.323842+00:00","updated_at":"2026-07-05T11:17:22.323842+00:00"}