Graph and download economic data for 4-Week Moving Average of Initial Claims (IC4WSA) from 1967-01-28 to 2025-03-15 about moving average, initial claims, 1-month, average, and USA.

United States - 4-Week Moving Average of Initial Claims was 227000.00000 Number in March 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 March of 2025.

Graph and download economic data for 4-Week Moving Average of Continued Claims (Insured Unemployment) (CC4WSA) from 1967-01-28 to 2025-03-08 about moving average, continued claims, 1-month, insurance, average, unemployment, and USA.

Jobless Claims 4-week Average in the United States decreased to 224 Thousand in March 22 from 228.75 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 1875750.00000 Number in March 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 March of 2025.

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 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 (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.

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.

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 MIP scoreboard 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.

United States Median Wage Growth: 3-Mo Mov Avg: Weekly data was reported at 4.200 % in Jan 2025. This records a decrease from the previous number of 4.300 % for Dec 2024. United States Median Wage Growth: 3-Mo Mov Avg: Weekly data is updated monthly, averaging 3.900 % from Mar 1997 (Median) to Jan 2025, with 335 observations. The data reached an all-time high of 7.000 % in Aug 2022 and a record low of 1.000 % in Oct 2009. United States Median Wage Growth: 3-Mo Mov Avg: Weekly data remains active status in CEIC and is reported by Federal Reserve Bank of Atlanta. The data is categorized under Global Database’s United States – Table US.G108: Atlanta Fed Wage Growth Tracker: 3-Month Moving Average.

Notification_date: data em que a infecção por dengue foi notificada pelo médico. Cumulative1W-: Sum of cases that occurred 1 week before notification date (considering Notification_date being day zero, the sum of the number of cases from day -1 to -7). Cumulative2W-: Sum of cases that occurred 2 weeks before notification date (considering Notification_date being day zero, the sum of the number of cases from day -8 to -14). Cumulative3W-: Sum of cases that occurred 3 weeks before notification date (considering Notification_date being day zero, the sum of the number of cases from day -15 to -22). Cumulative4W-: Sum of cases that occurred 4 weeks before notification date (considering Notification_date being day zero, the sum of the number of cases from day -23 to -29). Cumulative5W-: Sum of cases that occurred 5 weeks before notification date (considering Notification_date being day zero, the sum of the number of cases from day -30 to -36). Cumulative6W-: Sum of cases that occurred 6 weeks before notification date (considering Notification_date being day zero, the sum of the number of cases from day -37 to -44). Cumulative7W-: Sum of cases that occurred 7 weeks before notification date (considering Notification_date being day zero, the sum of the number of cases from day -45 to -51). Cumulative8W-: Sum of cases that occurred 8 weeks before notification date (considering Notification_date being day zero, the sum of the number of cases from day -52 to -58). Cumulative9W-: Sum of cases that occurred 9 weeks before notification date (considering Notification_date being day zero, the sum of the number of cases from day -59 to -65). Cumulative10W-: Sum of cases that occurred 10 weeks before notification date (considering Notification_date being day zero, the sum of the number of cases from day -66 to -72). Cumulative11W-: Sum of cases that occurred 11 weeks before notification date (considering Notification_date being day zero, the sum of the number of cases from day -73 to -79). Cumulative12W-: Sum of cases that occurred 12 weeks before notification date (considering Notification_date being day zero, the sum of the number of cases from day -80 to -86). Cumulative13W-: Sum of cases that occurred 13 weeks before notification date (considering Notification_date being day zero, the sum of the number of cases from day -87 to -93). Cumulative14W-: Sum of cases that occurred 14 weeks before notification date (considering Notification_date being day zero, the sum of the number of cases from day -94 to -100). Cumulative15W-: Sum of cases that occurred 15 weeks before notification date (considering Notification_date being day zero, the sum of the number of cases from day -101 to -107). Dengue_infefctions_sma7: confirmed dengue infections (moving average in the last 7 days). Dengue_cumulative: number of infections reported from 4/14/2007 to 3/10/2020. Population_inhab: estimated population in inhabitants. Population_density_inhabpkm2: demographic density in inhabitants per km2. Precipitation_mm_sma7: average daily rain precipitation in mm (moving average in the last 7 days). Daily_mean_temperaure_oC_sma7: average daily mean temperature in degrees Celsius (moving average in the last 7 days). Daily_minimum_temperaure_oC_sma7: average daily minimum temperature in degrees Celsius (moving average in the last 7 days). Relative_humidity_sma7: average daily relative humidity in % (moving average in the last 7 days). Wind_speed_mps_7sma: average daily wind speed in m/s (moving average in the last 7 days). Cumulative1W+_Target_1: Sum of cases that occurred in week 1 following the notification date (considering Notification_date being day zero, the sum of the number of cases from day +1 to +7). Cumulative2W+_Target_2: Sum of cases that occurred in week 2 following the notification date (considering Notification_date being day zero, the sum of the number of cases from day +8 to +14). Cumulative3W+_Target_3: Sum of cases that occurred in week 3 following the notification date (considering Notification_date being day zero, the sum of the number of cases from day +15 to +22). Cumulative4W+_Target_4: Sum of cases that occurred in week 4 following the notification date (considering Notification_date being day zero, the sum of the number of cases from day +23 to +29). Cumulative5W+_Target_5: Sum of cases that occurred in week 5 following the notification date (considering Notification_date being day zero, the sum of the number of cases from day +30 to +36). Cumulative6W+_Target_6: Sum of cases that occurred in week 6 following the notification date (considering Notification_date being day zero, the sum of the number of cases from day +37 to +44). Cumulative7W+_Target_7: Sum of cases that occurred in week 7 following the notification date (considering Notification_date being day zero, the sum of the number of cases from day +45 to +51). Cumulative8W+_Target_8: Sum of cases that occurred in week 8 following the notification date (considering...

This statistic displays information on the development of the average consumer book price in the United Kingdom (UK) as a four week moving average from October 16, 2012 to June 26, 2015. The average consumer book price as of June 26, 2015 was 7.48 British pounds. During the period of consideration, average prices generally ranged between seven and eight British pounds per book. Book prices peaked in September 2013 at 8.93 British pounds per copy. The lowest average price was registered in January of the same year at 6.74 British pounds. The UK book market is one of the largest worldwide. Among the country's leading publishing groups are Bertelsmann (Penguin Random House/Transworld), Hachette Livre (Headline/Hodder/Little Brown/Orion) and the News Corporation (HarperCollins).

Youth footballers need to be developed to meet the technical, tactical, and physical demands of professional level competition, ensuring that the transition between competition levels is successful. To quantify the physical demands, peak match intensities have been measured across football competition tiers, with team formations and tactical approaches shown to influence these physical demands. To date, no research has directly compared the physical demands of elite youth and professional footballers from a single club utilising common formations and tactical approaches. The current study quantified the total match and peak match running demands of youth and professional footballers from a single Australian A-League club. GPS data were collected across a single season from both a professional (n = 19; total observations = 199; mean ± SD; 26.7 ± 4.0 years) and elite youth (n = 21; total observations = 59; 17.9 ± 1.3 years) team. Total match demands and peak match running demands (1–10 min) were quantified for measures of total distance, high-speed distance [>19.8 km·h-1] and average acceleration. Linear mixed models and effect sizes identified differences between competition levels. No differences existed between competition levels for any total match physical performance metric. Peak total and high-speed distances demands were similar between competitions for all moving average durations. Interestingly, peak average acceleration demands were lower (SMD = 0.63–0.69) in the youth players across all moving average durations. The data suggest that the development of acceleration and repeat effort capacities is crucial in youth players for them to transition into professional competition.

Six alternative measures of labor underutilization have long been available on a monthly basis from the Current Population Survey (CPS) for the United States as a whole. They are published in the Bureau of Labor Statistics' monthly Employment Situation news release. (See table 15.) The official concept of unemployment (as measured in the CPS by U-3 in the U-1 to U-6 range of alternatives) includes all jobless persons who are available to take a job and have actively sought work in the past four weeks. This concept has been thoroughly reviewed and validated since the inception of the CPS in 1940. The other measures are provided to data users and analysts who want more narrowly (U-1 and U-2) or broadly (U-4 through U-6) defined measures.

BLS is committed to updating the alternative measures data for states on a 4-quarter moving-average basis. The use of 4-quarter averages increases the reliability of the CPS estimates, which are based on relatively small sample sizes at the state level, and eliminates seasonality. Due to the inclusion of lagged quarters, the state alternative measures may not fully reflect the current status of the labor market. The analysis that follows pertains to the 2023 annual averages. Data are also available for prior time periods back to 2003.

Parameter estimates based on a negative binomial regression using median price and a two-week moving average of the Google Trends swine flu index (first part).

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 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 (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.

This dataset contains a 30-year rolling average of annual average minimum and maximum temperatures 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 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 (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.

NHB: Market Price for Under-Cosntruction Properties: FQMA: Maharashtra: Nashik: Carpet Area Price: 646-1184 Sq Ft data was reported at 6,448.000 INR/sq ft in Sep 2024. This records an increase from the previous number of 6,375.000 INR/sq ft for Jun 2024. NHB: Market Price for Under-Cosntruction Properties: FQMA: Maharashtra: Nashik: Carpet Area Price: 646-1184 Sq Ft data is updated quarterly, averaging 5,866.500 INR/sq ft from Jun 2013 (Median) to Sep 2024, with 46 observations. The data reached an all-time high of 6,448.000 INR/sq ft in Sep 2024 and a record low of 4,620.000 INR/sq ft in Jun 2013. NHB: Market Price for Under-Cosntruction Properties: FQMA: Maharashtra: Nashik: Carpet Area Price: 646-1184 Sq Ft data remains active status in CEIC and is reported by National Housing Bank. The data is categorized under India Premium Database’s Construction and Property – Table IN.EA013: Housing Price Index: National Housing Bank: Market Price for Under-Construction Properties: Carpet Area Price: Four Quarter Moving Average.

Graph and download economic data for 3-Month Moving Average of Unweighted Median Weekly Wage Growth: Overall (FRBATLWGT3MMAUMWWGO) from Mar 1997 to Feb 2025 about growth, moving average, 3-month, average, wages, median, and USA.

NHB: Assessment Price: FQMA: Maharashtra: Nagpur: Carpet Unit data was reported at 901.000 Unit in Sep 2024. This records an increase from the previous number of 737.000 Unit for Jun 2024. NHB: Assessment Price: FQMA: Maharashtra: Nagpur: Carpet Unit data is updated quarterly, averaging 654.000 Unit from Jun 2013 (Median) to Sep 2024, with 46 observations. The data reached an all-time high of 1,139.000 Unit in Mar 2021 and a record low of 126.000 Unit in Jun 2020. NHB: Assessment Price: FQMA: Maharashtra: Nagpur: Carpet Unit data remains active status in CEIC and is reported by National Housing Bank. The data is categorized under India Premium Database’s Construction and Property – Table IN.EA007: Housing Price Index: National Housing Bank: Assessment Price: Carpet Area Price: Four Quarter Moving Average.

NHB: Assessment Price: FQMA: Haryana: Faridabad: Carpet Unit data was reported at 950.000 Unit in Sep 2024. This records an increase from the previous number of 859.000 Unit for Jun 2024. NHB: Assessment Price: FQMA: Haryana: Faridabad: Carpet Unit data is updated quarterly, averaging 492.500 Unit from Jun 2013 (Median) to Sep 2024, with 46 observations. The data reached an all-time high of 1,103.000 Unit in Mar 2022 and a record low of 102.000 Unit in Mar 2017. NHB: Assessment Price: FQMA: Haryana: Faridabad: Carpet Unit data remains active status in CEIC and is reported by National Housing Bank. The data is categorized under India Premium Database’s Construction and Property – Table IN.EA007: Housing Price Index: National Housing Bank: Assessment Price: Carpet Area Price: Four Quarter Moving Average.