Graph and download economic data for 4-Week Moving Average of Initial Claims (IC4WSA) from 1967-01-28 to 2025-09-20 about moving average, initial claims, 1-month, average, and USA.

View weekly updates and historical trends for US 4-Week Moving Average of Initial Claims for Unemployment Insurance. from United States. Source: Departmen…

Graph and download economic data for 4-Week Moving Average of Continued Claims (Insured Unemployment) (CC4WSA) from 1967-01-28 to 2025-09-13 about continued claims, moving average, 1-month, insurance, average, unemployment, and USA.

United States - 4-Week Moving Average of Initial Claims was 237500.00000 Number in September of 2025, according to the United States Federal Reserve. Historically, United States - 4-Week Moving Average of Initial Claims reached a record high of 5288250.00000 in April of 2020 and a record low of 179000.00000 in May of 1969. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - 4-Week Moving Average of Initial Claims - last updated from the United States Federal Reserve on October of 2025.

4-Week Moving Average of Initial Claims - Historical chart and current data through 2025.

Jobless Claims 4-week Average in the United States decreased to 237.50 Thousand in September 20 from 240.25 Thousand in the previous week. This dataset provides - United States Jobless Claims 4-week Average- actual values, historical data, forecast, chart, statistics, economic calendar and news.

United States - 4-Week Moving Average of Continued Claims (Insured Unemployment) was 1930000.00000 Number in September of 2025, according to the United States Federal Reserve. Historically, United States - 4-Week Moving Average of Continued Claims (Insured Unemployment) reached a record high of 21243000.00000 in May of 2020 and a record low of 998250.00000 in June of 1969. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - 4-Week Moving Average of Continued Claims (Insured Unemployment) - last updated from the United States Federal Reserve on October of 2025.

4-Week Moving Average Jobless Claims - Historical chart and current data through 2025.

Blockchain data query: Curve - top 10 most bought $ amount - last 30 days

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This dataset contains 30-year rolling averages of annual average minimum and maximum temperatures across all four models and two greenhouse gas (RCP) scenarios in the four model ensemble. The year identified for a 30 year rolling average is the mid-point of the 30-year average. eg. The year 2050 includes the values from 2036 to 2065.

The downscaling and selection of models for inclusion in ten and four model ensembles is described in 'https://www.energy.ca.gov/sites/default/files/2019-11/Projections_CCCA4-CEC-2018-006_ADA.pdf#page=11' rel='nofollow ugc'>Pierce et al. 2018, but summarized here. Thirty two global climate models (GCMs) were identified to meet the modeling requirements. From those, ten that closely simulate California’s climate were selected for additional analysis ('https://www.energy.ca.gov/sites/default/files/2019-11/Projections_CCCA4-CEC-2018-006_ADA.pdf#page=11' rel='nofollow ugc'>Table 1, Pierce et al. 2018) and to form a ten model ensemble. From the ten model ensemble, four models, forming a four model ensemble, were identified to provide coverage of the range of potential climate outcomes in California. The models in the four model ensemble and their general climate projection for California are:

• HadGEM2-ES (warm/dry),
• CanESM2 (average),
• CNRM-CM5 (cooler/wetter),
• and MIROC5 the model least like the others to improve coverage of the range of outcomes.

These data were downloaded from Cal-Adapt and prepared for use within CA Nature by California Natural Resource Agency and ESRI staff.

Cal-Adapt. (2018). LOCA Derived Data [GeoTIFF]. Data derived from LOCA Downscaled CMIP5 Climate Projections. Cal-Adapt website developed by University of California at Berkeley’s Geospatial Innovation Facility under contract with the California Energy Commission. Retrieved 0 from https://cal-adapt.org/

Pierce, D. W., J. F. Kalansky, and D. R. Cayan, (Scripps Institution of Oceanography). 2018. Climate, Drought, and Sea Level Rise Scenarios for the Fourth California Climate Assessment. California’s Fourth Climate Change Assessment, California Energy Commission. Publication Number: CNRA-CEC-2018-006.

This dataset contains 30-year rolling average of annual average precipitation across all four models and two greenhouse gas (RCP) scenarios in the four model ensemble. The year identified for a 30 year rolling average is the mid-point of the 30-year average. eg. The year 2050 includes the values from 2036 to 2065.

The downscaling and selection of models for inclusion in ten and four model ensembles is described in 'https://www.energy.ca.gov/sites/default/files/2019-11/Projections_CCCA4-CEC-2018-006_ADA.pdf#page=11' rel='nofollow ugc'>Pierce et al. 2018, but summarized here. Thirty two global climate models (GCMs) were identified to meet the modeling requirements. From those, ten that closely simulate California’s climate were selected for additional analysis ('https://www.energy.ca.gov/sites/default/files/2019-11/Projections_CCCA4-CEC-2018-006_ADA.pdf#page=11' rel='nofollow ugc'>Table 1, Pierce et al. 2018) and to form a ten model ensemble. From the ten model ensemble, four models, forming a four model ensemble, were identified to provide coverage of the range of potential climate outcomes in California. The models in the four model ensemble and their general climate projection for California are:

• HadGEM2-ES (warm/dry),
• CanESM2 (average),
• CNRM-CM5 (cooler/wetter),
• and MIROC5 the model least like the others to improve coverage of the range of outcomes.

These data were downloaded from Cal-Adapt and prepared for use within CA Nature by California Natural Resource Agency and ESRI staff.

Cal-Adapt. (2018). LOCA Derived Data [GeoTIFF]. Data derived from LOCA Downscaled CMIP5 Climate Projections. Cal-Adapt website developed by University of California at Berkeley’s Geospatial Innovation Facility under contract with the California Energy Commission. Retrieved from https://cal-adapt.org/

Pierce, D. W., J. F. Kalansky, and D. R. Cayan, (Scripps Institution of Oceanography). 2018. Climate, Drought, and Sea Level Rise Scenarios for the Fourth California Climate Assessment. California’s Fourth Climate Change Assessment, California Energy Commission. Publication Number: CNRA-CEC-2018-006.

Number of persons in the labour force (employment and unemployment), unemployment rate, participation rate and employment rate by census metropolitan area. Data are presented for 12 months earlier, previous month and current month, as well as year-over-year and month-to-month level change and percentage change. Data are also available for the standard error of the estimate, the standard error of the month-to-month change and the standard error of the year-over-year change.

The unemployment rate is the number of unemployed persons as a percentage of the labour force (the total number of people employed and unemployed) based on International Labour Office (ILO) definition. Unemployed persons comprise persons aged 15 to 74 who fulfil all three following conditions: - are without work during the reference week; - are available to start work within the next two weeks; - have been actively seeking work in the past four weeks or have already found a job to start within the next three months. The indicator monitors high and persistent rates of unemployment and it helps to better understand the potential severity of macroeconomic imbalances. It points towards a potential misallocation of resources and general lack of adjustment capacity in the economy. The indicator is the three-year backward moving average, i.e. the data for year Y is the arithmetic average of data for years Y, Y-1 and Y-2. It is calculated: [URt+URt-1+URt-2]/3. The indicative threshold is 10%. The data source is the quarterly EU Labour Force Survey (EU LFS). The EU LFS covers the resident population in private households.

Number of persons in the labour force (employment and unemployment) and not in the labour force, unemployment rate, participation rate and employment rate by economic region, last 5 months. Data are also available for the standard error of the estimate and the standard error of the year-over-year change.

Number of Civilians Unemployed for 15 Weeks or Longer for Maine was 4400.00000 Persons, 4-Qtr. Moving Average in July of 2024, according to the United States Federal Reserve. Historically, Number of Civilians Unemployed for 15 Weeks or Longer for Maine reached a record high of 31600.00000 in April of 2010 and a record low of 4400.00000 in July of 2024. Trading Economics provides the current actual value, an historical data chart and related indicators for Number of Civilians Unemployed for 15 Weeks or Longer for Maine - last updated from the United States Federal Reserve on September of 2025.

This dataset contains a 30-year rolling average of annual average precipitation from the four models and two greenhouse gas (RCP) scenarios included in the four model ensemble for the years 1950-2099. The year identified is the mid-point of the 30-year average. eg. The year 2050 includes the values from 2036 to 2065.

The downscaling and selection of models for inclusion in ten and four model ensembles is described in 'https://www.energy.ca.gov/sites/default/files/2019-11/Projections_CCCA4-CEC-2018-006_ADA.pdf#page=11' rel='nofollow ugc'>Pierce et al. 2018, but summarized here. Thirty two global climate models (GCMs) were identified to meet the modeling requirements. From those, ten that closely simulate California’s climate were selected for additional analysis ('https://www.energy.ca.gov/sites/default/files/2019-11/Projections_CCCA4-CEC-2018-006_ADA.pdf#page=11' rel='nofollow ugc'>Table 1, Pierce et al. 2018) and to form a ten model ensemble. From the ten model ensemble, four models, forming a four model ensemble, were identified to provide coverage of the range of potential climate outcomes in California. The models in the four model ensemble and their general climate projection for California are:

• HadGEM2-ES (warm/dry),
• CanESM2 (average),
• CNRM-CM5 (cooler/wetter),
• and MIROC5 the model least like the others to improve coverage of the range of outcomes.

These data were downloaded from Cal-Adapt and prepared for use within CA Nature by California Natural Resource Agency and ESRI staff.

Cal-Adapt. (2018). LOCA Derived Data [GeoTIFF]. Data derived from LOCA Downscaled CMIP5 Climate Projections. Cal-Adapt website developed by University of California at Berkeley’s Geospatial Innovation Facility under contract with the California Energy Commission. Retrieved from https://cal-adapt.org/

Pierce, D. W., J. F. Kalansky, and D. R. Cayan, (Scripps Institution of Oceanography). 2018. Climate, Drought, and Sea Level Rise Scenarios for the Fourth California Climate Assessment. California’s Fourth Climate Change Assessment, California Energy Commission. Publication Number: CNRA-CEC-2018-006.

Number of Civilians Unemployed for 15 Weeks or Longer for Tennessee was 47000.00000 Persons, 4-Qtr. Moving Average in July of 2024, according to the United States Federal Reserve. Historically, Number of Civilians Unemployed for 15 Weeks or Longer for Tennessee reached a record high of 185100.00000 in April of 2010 and a record low of 29300.00000 in October of 2018. Trading Economics provides the current actual value, an historical data chart and related indicators for Number of Civilians Unemployed for 15 Weeks or Longer for Tennessee - last updated from the United States Federal Reserve on September of 2025.

Number of persons in the labour force (employment and unemployment) and not in the labour force, unemployment rate, participation rate and employment rate by territory, gender and age group. Data are also available for the standard error of the estimate, the standard error of the month-to-month change and the standard error of the year-over-year change.

Files containing data in Figures 1-7 in the manuscript Korhonen N. et al. "The probabilistic skill of Extended-Range Heat wave forecasts over Europe". Files and their contents: Fig1a_HW_lower_threshold_ERA5_all_years.nc Data of Fig1a: The lower thresholds of heat wave days: the 90th percentile of the 5-day moving average temperature in summers 2000-2019 of the ERA5 reanalyses (Hersbach, et al. 2020). Fig1b_HW_lower_threshold_ERA5_excl2010.nc Data of Fig1b: The lower thresholds of heat wave days: the 90th percentile of the 5-day moving average temperature in summers 2000-2009 and 2011-2019 of the ERA5 reanalyses (Hersbach, et al. 2020). Fig1d_HW_lower_threshold_EC_fc_1wk_all_years.nc Data of Fig1d: The lower thresholds of heat wave days: the 90th percentile of the 5-day moving average temperature in summers 2000-2019 of the ensembles of the extended range hindcasts of the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS; Cycles 46r1 and 47r1; Vitart, 2014) in forecast week 1. Fig1e_HW_lower_threshold_EC_fc_1wk_excl2010.nc Data of Fig1e: The lower thresholds of heat wave days: the 90th percentile of the 5-day moving average temperature in summers 2000-2009 and 2011-2019 of the ensembles of the extended range hindcasts of the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS; Cycles 46r1 and 47r1; Vitart, 2014) in forecast week 1. Fig1g_HW_lower_threshold_EC_fc_2wk_all_years.nc Data of Fig1g: The lower thresholds of heat wave days: the 90th percentile of the 5-day moving average temperature in summers 2000-2019 of the ensembles of the ECMWF’s hindcasts in forecast week 2. Fig1h_HW_lower_threshold_EC_fc_2wk_excl2010.nc Data of Fig1h: The lower thresholds of heat wave days: the 90th percentile of the 5-day moving average temperature in summers 2000-2009 and 2011-2019 of the ensembles of the ECMWF’s hindcasts in forecast week 2. Fig1j_HW_lower_threshold_EC_fc_3wk_all_years.nc Data of Fig1j: The lower thresholds of heat wave days: the 90th percentile of the 5-day moving average temperature in summers 2000-2019 of the ensembles of the ECMWF’s hindcasts in forecast week 3. Fig1k_HW_lower_threshold_EC_fc_3wk_excl2010.nc Data of Fig1k: The lower thresholds of heat wave days: the 90th percentile of the 5-day moving average temperature in summers 2000-2009 and 2011-2019 of the ensembles of the ECMWF’s hindcasts in forecast week 3. Fig1m_HW_lower_threshold_EC_fc_4wk_all_years.nc Data of Fig1m: The lower thresholds of heat wave days: the 90th percentile of the 5-day moving average temperature in summers 2000-2019 of the ensembles of the ECMWF’s hindcasts in forecast week 4. Fig1n_HW_lower_threshold_EC_fc_4wk_excl2010.nc Data of Fig1n: The lower thresholds of heat wave days: the 90th percentile of the 5-day moving average temperature in summers 2000-2009 and 2011-2019 of the ensembles of the ECMWF’s hindcasts in forecast week 4. Fig2a_LHWMD_ERA5.nc Data of Fig2a: The duration of the longest period of heat wave days in each grid point over Europe defined from the ERA5 reanalysis data of summers 2000-2019. Fig2b_LHWMD_ERA5.nc Data of Fig2b: The duration of the longest period of heat wave days in each grid point over Europe defined from the ERA5 reanalysis data of summers 2000-2009 and 2011-2019. Fig2c_HWNP_ERA5.nc Data of Fig2c: the number of periods with heat wave days in the ERA5 reanalyses during 2000-2019. Fig2d_HWNP_ERA5.nc Data of Fig2d: the number of periods with heat wave days in the ERA5 reanalyses during 2000-2009 and 2011-2019. Fig3a_sharpness_diagram.txt Data of the sharpness diagram in Fig. 3a. Fig3b_reliability_diagram.txt Data of the reliability diagram in Fig. 3b. Fig4a_BSS_Week1_All_hindcasts.nc Fig4b_BSS_Week1_excluding_longest_HW.nc Fig4c_BSS_Week1_excl2010.nc Fig4d_BSS_Week2_All_hindcasts.nc Fig4e_BSS_Week2_excluding_longest_HW.nc Fig4f_BSS_Week2_excl2010.nc Fig4g_BSS_Week3_All_hindcasts.nc Fig4h_BSS_Week3_excluding_longest_HW.nc Fig4i_BSS_Week3_excl2010.nc Fig4j_BSS_Week4_All_hindcasts.nc Fig4k_BSS_Week4_excluding_longest_HW.nc Fig4l_BSS_Week4_excl2010.nc Data of the BSS in Figures 4a-l. Figure5a_ForecastWeek1.txt Figure5b_ForecastWeek2.txt Figure5c_ForecastWeek3.txt Figure5d_ForecastWeek4.txt Data of the boxplots in Figures 5a-d. Figure6a_ForecastWeek1.txt Figure6b_ForecastWeek2.txt Figure6c_ForecastWeek3.txt Figure6d_ForecastWeek4.txt Data of the boxplots in Figures 6a-d. Figure7a_ForecastWeek1.txt Figure7b_ForecastWeek2.txt Figure7c_ForecastWeek3.txt Figure7d_ForecastWeek4.txt Data of the boxplots in Figures 7a-d. Hersbach, H., Bell, B., Berrisford, P., et al.: The ERA5 Global Reanalysis, Quart. J. Roy. Meteor. Soc., 146, 1999-2049, doi:10.1002/qj.3803, 2020. Vitart F.: Evolution of ECMWF sub-seasonal forecast skill scores, Q. J. R. Meteorol. Soc., 140, 1889–1899, doi: 10.1002/qj.2256, 2014.