{"url":"/dataset/tsfm-scalinglaws-dataset","name":"TSFM-ScalingLaws-Dataset","full_name":null,"description_markdown":"TSFM-ScalingLaws-Dataset\r\n\r\nThis is the dataset for the paper Towards Neural Scaling Laws for Time Series Foundation Models.\r\n\r\nWe selected some high-quality (SNR > 20), domain-balanced, and low-redundancy data from Lotsa and UTS datasets for TSFM pre-training. These datasets contain three sizes: 16B, 1B, 100M, and 10M.\r\n\r\nCode: https://github.com/Qingrenn/TSFM-ScalingLaws\r\n\r\nWell-trained models: https://huggingface.co/PeacefulData/TSFM-ScalingLaws-Checkpoints","description_withheld":null,"homepage":"https://huggingface.co/datasets/Qingren/TSFM-ScalingLaws-Dataset","introduced_date":"2024-10-16","introduced_date_note":null,"introduced_by":{"paper":"/paper/towards-neural-scaling-laws-for-time-series","title":"Towards Neural Scaling Laws for Time Series Foundation Models","first_author":"Qingren Yao","url":null},"license":null,"modalities":[{"name":"Time series","url":"/datasets/modality/time-series"}],"tasks":[{"name":"Time Series Prediction","url":"/task/time-series-prediction","datasets_with_task":"/datasets/task/time-series-prediction"}],"languages":[],"variants":["TSFM-ScalingLaws-Dataset"],"data_loaders":[],"num_papers_in_archive":1,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}