Consumer Price Index CPI in the United States increased to 321.47 points in May from 320.80 points in April of 2025. This dataset provides the latest reported value for - United States Consumer Price Index (CPI) - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Treasury Inflation-Protected Securities, also known as TIPS, are securities whose principal is tied to the Consumer Price Index. With inflation, the principal increases. With deflation, it decreases. When the security matures, the U.S. Treasury pays the original or adjusted principal, whichever is greater.

Geotail Weimer propagated solar wind data and linearly interpolated to have the measurements on the minute at 60 s resolution CPI data in GSE coordinates. This data set consists of propagated solar wind data that has first been propagated to a position just outside of the nominal bow shock (about 17, 0, 0 Re) and then linearly interpolated to 1 min resolution using the interp1.m function in MATLAB. The input data for this data set is a 1 min resolution processed solar wind data constructed by Dr. J.M. Weygand. The method of propagation is similar to the minimum variance technique and is outlined in Dan Weimer et al. [2003; 2004]. The basic method is to find the minimum variance direction of the magnetic field in the plane orthogonal to the mean magnetic field direction. This minimum variance direction is then dotted with the difference between final position vector minus the original position vector and the quantity is divided by the minimum variance dotted with the solar wind velocity vector, which gives the propagation time. This method does not work well for shocks and minimum variance directions with tilts greater than 70 degrees of the sun-earth line. This data set was originally constructed by Dr. J.M. Weygand for Prof. R.L. McPherron, who was the principle investigator of two National Science Foundation studies: GEM Grant ATM 02-1798 and a Space Weather Grant ATM 02-08501. These data were primarily used in superposed epoch studies References: Weimer, D. R. (2004), Correction to ‘‘Predicting interplanetary magnetic field (IMF) propagation delay times using the minimum variance technique,’’ J. Geophys. Res., 109, A12104, doi:10.1029/2004JA010691. Weimer, D.R., D.M. Ober, N.C. Maynard, M.R. Collier, D.J. McComas, N.F. Ness, C. W. Smith, and J. Watermann (2003), Predicting interplanetary magnetic field (IMF) propagation delay times using the minimum variance technique, J. Geophys. Res., 108, 1026, doi:10.1029/2002JA009405.

Consumer price indexes (CPIs) are index numbers that measure changes in the prices of goods and services purchased or otherwise acquired by households, which households use directly, or indirectly, to satisfy their own needs and wants. In practice, most CPIs are calculated as weighted averages of the percentage price changes for a specified set, or ‘‘basket’’, of consumer products, the weights reflecting their relative importance in household consumption in some period. CPIs are widely used to index pensions and social security benefits. CPIs are also used to index other payments, such as interest payments or rents, or the prices of bonds. CPIs are also commonly used as a proxy for the general rate of inflation, even though they measure only consumer inflation. They are used by some governments or central banks to set inflation targets for purposes of monetary policy. The price data collected for CPI purposes can also be used to compile other indices, such as the price indices used to deflate household consumption expenditures in national accounts, or the purchasing power parities used to compare real levels of consumption in different countries.

In an effort to further coordinate and harmonize the collection of CPI data, the international organizations agreed that the International Monetary Fund (IMF) and the Organisation for Economic Cooperation and Development (OECD) would assume responsibility for the international collection and dissemination of national CPI data. Under this data collection initiative, countries are reporting the aggregate all items index; more detailed indexes and weights for 12 subgroups of consumption expenditure (according to the so-called COICOP-classification), and detailed metadata. These detailed data represent a valuable resource for data users throughout the world and this portal would not be possible without the ongoing cooperation of all reporting countries. In this effort, the OECD collects and validates the data for their member countries, including accession and key partner countries, whereas the IMF takes care of the collection of data for all other countries.

This dataset provides values for CONSUMER PRICE INDEX CPI reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

The Cloud Particle Imager captures high-resolution images (pixel size 2.3 microns) using a CCD camera. The CPI has two Particle Detector Sensor lasers that instruct the camera to capture an image. For RICO the CPI was put on "random mode" during cloud work usually between the 2 sets of circles during the day's C-130 flight. In random mode the CCD camera flashes at its maximum frame rate usually 74 frames per second. This enabled the CPI to capture much more particle images since it will image anything within the sample volume at that moment. It also allows for a better determination of the sample volume and concentration since it is constantly recording and not relying on lasers to detect particles beforehand, which cannot detect every particle passing through the sample volume. The sample area of the CPI is 1024x1024 pixels(@2.3 micron = 2.4 microns squared).

Consumer Price Index CPI in European Union decreased to 133.23 points in May from 133.24 points in April of 2025. This dataset provides - European Union Consumer Price Index (CPI) - actual values, historical data, forecast, chart, statistics, economic calendar.

Context:

The Consumer Price Indexes (CPI) program produces monthly data on changes in the prices paid by urban consumers for a representative basket of goods and services. It is a useful way to compare changes in the economy across time.

Content:

This data covers Jan 1913-May 2017, and is normalized to “CPI-U all items 1982-84=100, not seasonally adjusted”. Fields include time of measurement and CPI score.

Acknowledgements:

This dataset was compiled on behalf of the Bureau of Labor Statistics (BLS) via Colorado Department of Labor & Employment (CDLE) and hosted on data.colorado.gov.

Inspiration:

• What periods of time have seen the highest/lowest CPI?
• When has inflation been the worse?
• Can you predict present CPI?

The Consumer Price Index (CPI) is a key economic indicator used by policymakers worldwide to monitor inflation and guide monetary policy decisions. In Korea, the CPI significantly impacts decisions on interest rates, fiscal policy frameworks, and the Bank of Korea’s strategies for economic stability. Given its importance, accurately forecasting the Total CPI is crucial for informed decision-making. Achieving accurate estimation, however, presents several challenges. First, the Korean Total CPI is calculated as a weighted sum of 462 items grouped into 12 categories of goods and services. This heterogeneity makes it difficult to account for all variations in consumer behavior and price dynamics. Second, the monthly frequency of CPI data results in a relatively sparse time series, limiting the performance of the analysis. Furthermore, external factors such as policy changes and pandemics add further volatility to the CPI. To address these challenges, we propose a novel framework consisting of four key components: (1) a hybrid Convolutional Neural Network-Long Short-Term Memory mechanism designed to capture complex patterns in CPI data, enhancing estimation accuracy; (2) multivariate inputs that incorporate CPI component indices alongside auxiliary variables for richer contextual information; (3) data augmentation through linear interpolation to convert monthly data into daily data, optimizing it for highly parametrized deep learning models; and (4) sentiment index derived from Korean CPI-related news articles, providing insights into external factors influencing CPI fluctuations. Experimental results demonstrate that the proposed model outperforms existing approaches in CPI prediction, as evidenced by lower RMSE values. This improved accuracy has the potential to support the development of more timely and effective economic policies.

This dataset provides values for TOKYO CORE CPI reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

The Cloud Particle Imager captures high-resolution images (pixel size 2.3 microns) using a CCD camera. The CPI has two Particle Detector Sensor lasers that instruct the camera to capture an image. This dataset contains region of interest (roi) binary image files of cloud particles which can be viewed using the IDL software linked to below For all of the flights, the CPI makes as many *.roi files as it needs; each file can be maximum 92Mb size and then a new file is opened.

Annual Consumer Price Index (CPI) for most countries in the world. Reference year is 2005.

Consumer Price Index CPI in Germany increased to 121.80 points in May from 121.70 points in April of 2025. This dataset provides the latest reported value for - Germany Consumer Price Index (CPI) - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This dataset provides the latest food Inflation rate of India, as published by the Ministry of Statistics & Programme Implementation, along with historical trends.

Geotail linearly interpolated to have the measurements on the minute at 60 s resolution CPI data in GSE coordinates. This data set consists of processed solar wind data that has been linearly interpolated to 1 min resolution at the position of the spacecraft using the interp1.m function in MATLAB. This data set was originally constructed by Dr. J.M. Weygand for Prof. R.L. McPherron, who was the principle investigator of two National Science Foundation studies: GEM Grant ATM 02-1798 and a Space Weather Grant ATM 02-08501. These data were primarily used in superposed epoch studies and cross correlation studies on solar wind.

This dataset includes monthly, fiscal, and annual releases produced by BC Stats using data from the Consumer Price Index (CPI). Adapted from Statistics Canada, Consumer Price Index (CPI), accessed various times throughout 2024 and 2025. This does not constitute an endorsement by Statistics Canada of this product. For the specific month of access, please refer to the individual resource.

The Consumer Price Index (CPI) is a key economic indicator used by policymakers worldwide to monitor inflation and guide monetary policy decisions. In Korea, the CPI significantly impacts decisions on interest rates, fiscal policy frameworks, and the Bank of Korea’s strategies for economic stability. Given its importance, accurately forecasting the Total CPI is crucial for informed decision-making. Achieving accurate estimation, however, presents several challenges. First, the Korean Total CPI is calculated as a weighted sum of 462 items grouped into 12 categories of goods and services. This heterogeneity makes it difficult to account for all variations in consumer behavior and price dynamics. Second, the monthly frequency of CPI data results in a relatively sparse time series, limiting the performance of the analysis. Furthermore, external factors such as policy changes and pandemics add further volatility to the CPI. To address these challenges, we propose a novel framework consisting of four key components: (1) a hybrid Convolutional Neural Network-Long Short-Term Memory mechanism designed to capture complex patterns in CPI data, enhancing estimation accuracy; (2) multivariate inputs that incorporate CPI component indices alongside auxiliary variables for richer contextual information; (3) data augmentation through linear interpolation to convert monthly data into daily data, optimizing it for highly parametrized deep learning models; and (4) sentiment index derived from Korean CPI-related news articles, providing insights into external factors influencing CPI fluctuations. Experimental results demonstrate that the proposed model outperforms existing approaches in CPI prediction, as evidenced by lower RMSE values. This improved accuracy has the potential to support the development of more timely and effective economic policies.

United States Atlanta Fed Flexible CPI data was reported at -0.000 % in Apr 2025. This records an increase from the previous number of -0.006 % for Mar 2025. United States Atlanta Fed Flexible CPI data is updated monthly, averaging 0.003 % from Jan 1967 (Median) to Apr 2025, with 700 observations. The data reached an all-time high of 0.037 % in Sep 2005 and a record low of -0.057 % in Nov 2008. United States Atlanta Fed Flexible CPI 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.I: Sticky-Price CPI.

Consumer Price Index CPI in India increased to 194.20 points in June from 193 points in May of 2025. This dataset provides - India Consumer Price Index (CPI) - actual values, historical data, forecast, chart, statistics, economic calendar and news.

United States Atlanta Fed Core Sticky CPI data was reported at 0.003 % in Apr 2025. This records an increase from the previous number of 0.000 % for Mar 2025. United States Atlanta Fed Core Sticky CPI data is updated monthly, averaging 0.003 % from Jan 1967 (Median) to Apr 2025, with 700 observations. The data reached an all-time high of 0.012 % in Jun 1974 and a record low of -0.004 % in Apr 2020. United States Atlanta Fed Core Sticky CPI 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.I: Sticky-Price CPI.