{"url":"/dataset/finance","name":"Consumer Spendings","full_name":"Finance > US Economy > Consumer Spendings","description_markdown":"State-level data for the US economy through the lens of consumer spending (Credit/Debit Spending) .  The dataset is enriched with state-level Economic Dynamics and Policy Responses. Specifically, we further enriched the data with the state-level policies as an indication of extreme events (e.g., the state’s business closure order). \r\n\r\n- **Date**: The date of the data record.\r\n- **StateAbbr**: Abbreviation of the state.\r\n- **Population**: Population of the state.\r\n- **Spend_xxx**: Various spending metrics (e.g., `spend_acf`, `spend_aer`, `spend_all`). Spend_all (all categories)\r\n- **Policy**: Indicator of policy measures.\r\n- **lnDailyNewDeaths**, **DailyNewDeaths**: Logarithm and raw counts of daily new deaths.\r\n- **lnDailyNewCases**, **DailyNewCases**: Logarithm and raw counts of daily new cases.\r\n- **Deaths**, **Cases**: Cumulative counts.\r\n- **Day**, **Day_**, **DayOfYear**: Day-related information.","description_withheld":null,"homepage":"https://github.com/ashfarhangi/AA-Forecast/tree/main/dataset","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":"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":"Time Series Classification","url":"/task/time-series-classification","datasets_with_task":"/datasets/task/time-series-classification"},{"name":"Time Series Anomaly Detection","url":"/task/time-series-anomaly-detection","datasets_with_task":"/datasets/task/time-series-anomaly-detection"}],"languages":[],"variants":["Consumer Spendings"],"data_loaders":[],"num_papers_in_archive":2,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/time-series-forecasting-on-finance","task":"Time Series Forecasting","dataset_variant":"Consumer Spendings","rows":1,"metrics":["RMSE"],"first_row_in_archive_order":{"model":"TSE-SC","paper":"/paper/traffic-transformer-capturing-the-continuity","metrics":{"RMSE":"0.072"},"code_links":[]},"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}],"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."}