{"work":{"id":"a4d7bd2f-9620-47d6-8c8f-0f25283a9f57","openalex_id":"https://openalex.org/W4387796530","doi":"10.48550/arxiv.2310.10688","arxiv_id":"2310.10688","raw_key":null,"title":"A decoder-only foundation model for time-series forecasting","authors":null,"authors_text":"Abhimanyu Das, Weihao Kong, Rajat Sen, Yichen Zhou","year":2023,"venue":"cs.CL","abstract":"Motivated by recent advances in large language models for Natural Language Processing (NLP), we design a time-series foundation model for forecasting whose out-of-the-box zero-shot performance on a variety of public datasets comes close to the accuracy of state-of-the-art supervised forecasting models for each individual dataset. Our model is based on pretraining a patched-decoder style attention model on a large time-series corpus, and can work well across different forecasting history lengths, prediction lengths and temporal granularities.","external_url":"https://arxiv.org/abs/2310.10688","cited_by_count":42,"metadata_source":"pith","metadata_fetched_at":"2026-08-05T02:28:24.338817+00:00","pith_arxiv_id":"2310.10688","created_at":"2026-05-10T10:39:37.929331+00:00","updated_at":"2026-08-05T02:28:24.338817+00:00","title_quality_ok":true,"display_title":"A decoder-only foundation model for time-series forecasting","render_title":"A decoder-only foundation model for time-series forecasting"},"hub":{"state":{"work_id":"a4d7bd2f-9620-47d6-8c8f-0f25283a9f57","tier":"hub","tier_reason":"10+ Pith inbound or 1,000+ external citations","pith_inbound_count":44,"external_cited_by_count":42,"distinct_field_count":10,"first_pith_cited_at":"2024-03-12T16:53:54+00:00","last_pith_cited_at":"2026-07-07T14:47:46+00:00","author_build_status":"not_needed","summary_status":"needed","contexts_status":"needed","graph_status":"needed","ask_index_status":"not_needed","reader_status":"not_needed","recognition_status":"not_needed","updated_at":"2026-08-23T00:59:31.045059+00:00","tier_text":"hub"},"tier":"hub","role_counts":[{"context_role":"background","n":6},{"context_role":"baseline","n":3},{"context_role":"method","n":1}],"polarity_counts":[{"context_polarity":"background","n":6},{"context_polarity":"baseline","n":3},{"context_polarity":"use_method","n":1}],"runs":{},"summary":{},"graph":{},"authors":[]}}