{"url":"/dataset/hurricane","name":"Extreme Events > Natural Disasters > Hurricane","full_name":"Tourism > Finance > Sales Revenue","description_markdown":"A new spatio-temporal benchmark dataset (Hurricane), is suited for forecasting during extreme events and anomalies. The dataset is provided through the Florida Department of Revenue which provides the monthly sales revenue (2003-2020) for the tourism industry for all 67 counties of Florida which are prone to annual hurricanes. Furthermore, we aligned and joined the raw time series with the history of hurricane categories (i.e., event intensities) based on time for each county.\r\nNote that the hurricane category indicates the maximum sustained wind speed which can result in catastrophic damages as this number goes up (Category 1-6).","description_withheld":null,"homepage":"","introduced_date":"2022-08-21","introduced_date_note":null,"introduced_by":{"paper":"/paper/aa-forecast-anomaly-aware-forecast-for","title":"AA-Forecast: Anomaly-Aware Forecast for Extreme Events","first_author":"Ashkan Farhangi","url":null},"license":null,"modalities":[{"name":"Tabular","url":"/datasets/modality/tabular"},{"name":"Time series","url":"/datasets/modality/time-series"}],"tasks":[{"name":"Time Series Analysis","url":"/task/time-series","datasets_with_task":"/datasets/task/time-series"},{"name":"Time Series Forecasting","url":"/task/time-series-forecasting","datasets_with_task":"/datasets/task/time-series-forecasting"},{"name":"Weather Forecasting","url":"/task/weather-forecasting","datasets_with_task":"/datasets/task/weather-forecasting"},{"name":"Univariate Time Series Forecasting","url":"/task/univariate-time-series-forecasting","datasets_with_task":"/datasets/task/univariate-time-series-forecasting"},{"name":"Time Series Prediction","url":"/task/time-series-prediction","datasets_with_task":"/datasets/task/time-series-prediction"},{"name":"Hurricane Forecasting","url":"/task/hurricane-forecasting","datasets_with_task":"/datasets/task/hurricane-forecasting"},{"name":"Irregular Time Series","url":"/task/irregular-time-series","datasets_with_task":"/datasets/task/irregular-time-series"}],"languages":[],"variants":["Extreme Events > Natural Disasters > Hurricane"],"data_loaders":[{"repo":"https://github.com/ashfarhangi/aa-forecast","url":"https://github.com/ashfarhangi/aa-forecast","frameworks":["tf","pytorch","jax"]}],"num_papers_in_archive":3,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/time-series-forecasting-on-hurricane","task":"Time Series Forecasting","dataset_variant":"Extreme Events > Natural Disasters > Hurricane","rows":2,"metrics":["RMSE"],"first_row_in_archive_order":{"model":"UberNN","paper":"/paper/deep-and-confident-prediction-for-time-series","metrics":{"RMSE":"0.453"},"code_links":[{"title":"PawaritL/BayesianLSTM","url":"https://github.com/PawaritL/BayesianLSTM"},{"title":"vincekellner/demandforecasting","url":"https://github.com/vincekellner/demandforecasting"},{"title":"GiorgioMorales/PredictionIntervals","url":"https://github.com/GiorgioMorales/PredictionIntervals"},{"title":"NISL-MSU/PredictionIntervals","url":"https://github.com/NISL-MSU/PredictionIntervals"},{"title":"ManjunathAdi/Seq2Seq_RNN","url":"https://github.com/ManjunathAdi/Seq2Seq_RNN"},{"title":"jsiloto/dengAI","url":"https://github.com/jsiloto/dengAI"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/traffic-transformer-capturing-the-continuity","title":"Traffic transformer: Capturing the continuity and periodicity of time series for traffic forecasting","date":"2020-06-11","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/deep-and-confident-prediction-for-time-series","title":"Deep and Confident Prediction for Time Series at Uber","date":"2017-09-06","rows_on_this_dataset":1,"code_links":6,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":2,"samples_unverified":0,"pointer_only_for_licence":2,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":1,"samples_harvested":2,"samples_ran":2,"samples_unverified":0,"pointer_only_for_licence":2,"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."}