{"url":"/dataset/metr-la-point-missing","name":"METR-LA Point Missing","full_name":null,"description_markdown":"The original dataset from [Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting](https://arxiv.org/abs/1707.01926) contains traffic readings collected from 207 loop detectors on highways in Los Angeles County, aggregated in 5 minutes intervals over four months between March 2012 and June 2012.\r\n\r\nThe __Point missing__ setting, introduced in [Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networks](https://arxiv.org/abs/2108.00298v3), is a variant for imputation in which 25% of data are masked out uniformly at random. Results on this dataset are assumed to be obtained __in-sample__, meaning that the test interval is used also for training, excluding data used for evaluation.","description_withheld":null,"homepage":"","introduced_date":"2021-07-31","introduced_date_note":null,"introduced_by":{"paper":"/paper/multivariate-time-series-imputation-by-graph","title":"Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networks","first_author":"Andrea Cini","url":null},"license":null,"modalities":[{"name":"Time series","url":"/datasets/modality/time-series"}],"tasks":[{"name":"Imputation","url":"/task/imputation","datasets_with_task":"/datasets/task/imputation"},{"name":"Multivariate Time Series Imputation","url":"/task/multivariate-time-series-imputation","datasets_with_task":"/datasets/task/multivariate-time-series-imputation"},{"name":"Traffic Data Imputation","url":"/task/traffic-data-imputation","datasets_with_task":"/datasets/task/traffic-data-imputation"}],"languages":[],"variants":["METR-LA Point Missing"],"data_loaders":[{"repo":"https://github.com/torchspatiotemporal/tsl","url":"https://github.com/torchspatiotemporal/tsl","frameworks":["pytorch"]}],"num_papers_in_archive":3,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/traffic-data-imputation-on-metr-la-point","task":"Traffic Data Imputation","dataset_variant":"METR-LA Point Missing","rows":2,"metrics":["MAE"],"first_row_in_archive_order":{"model":"GRIN","paper":"/paper/multivariate-time-series-imputation-by-graph","metrics":{"MAE":"1.91"},"code_links":[{"title":"torchspatiotemporal/tsl","url":"https://github.com/torchspatiotemporal/tsl"},{"title":"Graph-Machine-Learning-Group/grin","url":"https://github.com/Graph-Machine-Learning-Group/grin"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/multivariate-time-series-imputation-by-graph","title":"Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networks","date":"2021-07-31","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":10,"samples_ran":5,"samples_unverified":5,"pointer_only_for_licence":9,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/brits-bidirectional-recurrent-imputation-for","title":"BRITS: Bidirectional Recurrent Imputation for Time Series","date":"2018-05-27","rows_on_this_dataset":1,"code_links":5,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":4,"samples_ran":2,"samples_unverified":2,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-25T09:33:49+00:00","papers_with_samples":2,"samples_harvested":14,"samples_ran":7,"samples_unverified":7,"pointer_only_for_licence":9,"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."}