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.

Purpose and brief description The consumer price index is an economic indicator whose main task is to objectively reflect the price evolution over time for a basket of goods and services purchased by households and considered representative of their consumer habits. The index does not necessarily measure the price level of this basket for a specific period of time, but rather the fluctuation between two periods, the first one acting as basis for comparison. Moreover, this difference in the price level is not measured in absolute, but in relative terms. The consumer price index can be determined as a hundred times the ratio between the observed prices of a range of goods and services at a given time and the prices of the same goods and services, observed under the same circumstances during the reference period, chosen as basis for comparison. Price observations always take place in the same regions. Since 2014, the consumer price index has been a chain index in which the weighting reference period is regularly shifted and prices and quantities are no longer compared between the current period and a fixed reference period, but the current period is compared with an intermediate period. By multiplying these short-term indices, and so creating a chain, we get a long-term series with a fixed reference period. Population Belgian private households Data collection method and possible sampling Survey technique applied using a computer, based on the use of electronic questionnaires and laptops. Frequency Monthly. Timing of publication The results are available on the penultimate working day of the reference period. Definitions Weight (CPI): The weight represents the importance of the goods and services included in the CPI in the total expenditure patterns of the households. Weights are determined based on the household budget survey. Consumer price index (CPI): The consumer price index is an economic indicator whose main task is to objectively reflect the price evolution over time for a basket of goods and services purchased by households and considered representative of their consumer habits. Health index: The health index is derived from the consumer price index and has been published since January 1994. The current value of this index is determined by removing a number of products from the consumer price index product basket, in particular alcoholic beverages (bought in a shop or consumed in a bar), tobacco products and motor fuels except for LPG. Inflation: Inflation is defined as the ratio between the value of the consumer price index of a given month and the index of the same month the year before. Therefore, inflation measures the rhythm of the evolution of the overall price level. Consumer price index without petroleum products: This index is calculated by removing the following products from the consumer price index: butane, propane, liquid fuels and motor fuels. Consumer price index without energy products: This index is calculated by removing the following products from the consumer price index: electricity, natural gas, butane, propane, liquid fuels, solid fuels and motor fuels. Smoothed index: The smoothed health index, also called smoothed index (the average value of the health indexes of the last 4 months) is used as a basis for the indexation of retirement pensions, social security benefits and some salaries and wages. Public wages and social benefits are indexed as soon as the smoothed index reaches a given value, called the central index. The smoothed index is also called moving average. In order to perform a 2% index jump (laid down in the Law of 23 April 2015 on employment promotion), the smoothed health index has been temporarily blocked at its value of March 2015 (100.66). The smoothed health index was then reduced by 2% from April 2015. When the reduced smoothed health index (also called the reference index) had increased again by 2% or in other words when it had exceeded the value of 100.66, the index was no longer blocked. It occurred in April 2016. Since April 2016 the smoothed health index is calculated in the same manner as the reference index and therefore corresponds to the arithmetical mean of the health indexes of the last 4 months multiplied by a factor of 0.98. The central index is a predetermined threshold value against which the smoothed health index is compared. If the central index is reached or exceeded, there is an indexation of the wages and salaries or benefits. This indexation is proportional to the percentage between the old and the new central index. For the public sector and social benefits, the difference between the central indices always amounts to 2 %. Therefore, a 2 % indexation is applied every time the central index is reached. There are also collective labour agreements according to which the difference between the central indices amounts to 1 % or 1.5 %. The reaching of a central index then leads to an indexation of 1 % or 1,5 %. See also: https://bosa.belgium.

📈 Daily Historical Stock Price Data for CPI Card Group Inc. (2015–2025)

A clean, ready-to-use dataset containing daily stock prices for CPI Card Group Inc. from 2015-10-08 to 2025-05-28. This dataset is ideal for use in financial analysis, algorithmic trading, machine learning, and academic research.

  🗂️ Dataset Overview

Company: CPI Card Group Inc. Ticker Symbol: PMTS Date Range: 2015-10-08 to 2025-05-28 Frequency: Daily Total Records: 2423 rows (one per trading day)… See the full description on the dataset page: https://huggingface.co/datasets/khaledxbenali/daily-historical-stock-price-data-for-cpi-card-group-inc-20152025.

****Dataset Overview**** This dataset contains historical macroeconomic data, featuring key economic indicators in the United States. It includes important metrics such as the Consumer Price Index (CPI), Retail Sales, Unemployment Rate, Industrial Production, Money Supply (M2), and more. The dataset spans from 1993 to the present and includes monthly data on various economic indicators, processed to show their rate of change (either percentage or absolute difference, depending on the indicator).

provenance

The data in this dataset is sourced from the Federal Reserve Economic Data (FRED) database, hosted by the Federal Reserve Bank of St. Louis. FRED provides access to a wide range of economic data, including key macroeconomic indicators for the United States. My work involved calculating the rate of change (ROC) for each indicator and reorganizing the data into a more usable format for analysis. For more information and access to the full database, visit FRED's website.

Purpose and Use for the Kaggle Community:

This dataset is a valuable resource for data scientists, economists, and analysts interested in understanding macroeconomic trends, performing time series analysis, or building predictive models. With the rate of change included, users can quickly assess the growth or contraction in these indicators month-over-month. This dataset can be used for:

• Exploratory Data Analysis (EDA): Understanding historical economic trends. -Time Series Forecasting: Building models to predict future economic conditions. -Macroeconomic Analysis: Analyzing the relationship between various economic indicators. -Machine Learning Projects: Using the data as features to predict financial or economic outcomes. -By utilizing this dataset, users can perform in-depth analysis on the impact of macroeconomic changes, compare the historical performance of various indicators, and experiment with different time series forecasting techniques.

****Column Descriptions****

Year: The year of the observation.

Month: The month of the observation (1-12).

Industrial Production: Monthly data on the total output of US factories, mines, and utilities.

Manufacturers' New Orders: Durable Goods: Measures the value of new orders placed with manufacturers for durable goods, indicating future production activity.

Consumer Price Index (CPIAUCSL): A measure of the average change over time in the prices paid by urban consumers for a market basket of consumer goods and services.

Unemployment Rate: The percentage of the total labor force that is unemployed but actively seeking employment.

Retail Sales: The total receipts of retail stores, indicating consumer spending and economic activity.

Producer Price Index: Measures the average change over time in the selling prices received by domestic producers for their output.

Personal Consumption Expenditures (PCE): A measure of the prices paid by consumers for goods and services, used in calculating inflation.

National Home Price Index: A measure of changes in residential real estate prices across the country.

All Employees, Total Nonfarm: The number of nonfarm payroll employees, an important indicator of the labor market.

Labor Force Participation Rate: The percentage of the working-age population that is either employed or actively looking for work.

Federal Funds Effective Rate: The interest rate at which depository institutions lend reserve balances to other depository institutions overnight.

Building Permits: The number of building permits issued for residential and non-residential buildings, a leading indicator of construction activity.

Money Supply (M2): The total money supply, including cash, checking deposits, and easily convertible near money.

Personal Income: The total income received by individuals from all sources, including wages, investments, and government transfers.

Trade Balance: The difference between a country's imports and exports, indicating the net trade flow.

Consumer Sentiment: The index reflecting consumer sentiment and expectations for the future economic outlook.

Consumer Confidence: A measure of how optimistic or pessimistic consumers are regarding their expected financial situation and the economy.

Notes on Interest Rates Please note that for the Federal Funds Effective Rate (FEDFUNDS), the dataset includes the absolute change in basis points (bps), not the rate of change. This means that the dataset reflects the direct change in the interest rate rather than the percentage change month-over-month. The change is represented in basis points, where 1 basis point equals 0.01%.

This data table contains Consumer Price Index (CPI) monthly summary statistics for food categories, as well as year-to-date CPI data for Manitoba and other provincial jurisdictions in Canada. This data table contains Consumer Price Index (CPI) monthly summary statistics for food categories, as well as year-to-date (YTD) CPI data for Manitoba and other provincial jurisdictions in Canada. These data are displayed in the Manitoba Food Consumer Price Index Tables. The source of the information is the Statistics Canada Table 18-10-0004-01 Consumer Price Index, monthly, not seasonally adjusted. Data are updated monthly by Manitoba Agriculture from Statistics Canada sources. Fields included [Alias (Field Name): Field description] GEO (GEO): Province or territory name; lso includes Canada as a whole PRODUCT (PRODUCT): CPI product group; food categories include: meat; fish, seafood and other marine products; dairy products; eggs; bakery and cereal products; fruit, fruit preparations and nuts; vegetables and vegetable preparations; other food products and non-alcoholic beverages; all foods YEAR_1 (YEAR_1): Year value for previous year YEAR_2 (YEAR_2): Year value of current year MONTH (MONTH): Month name for the most recent month represented in the data MONTH_RANGE (MONTH_RANGE): Month range from January to the most recent month represented in the data CPI_1 (CPI_1): CPI value for the month indicated in MONTH and year indicated in YEAR_1 CPI_2 (CPI_2): CPI value for the month indicated in MONTH and year indicated in YEAR_2 YTD_AVERAGE_1 (YTD_AVERAGE_1): Average CPI value for the month range indicated in MONTH_RANGE and year indicated in YEAR_1 YTD_AVERAGE_2 (YTD_AVERAGE_2): Average CPI value for the month range indicated in MONTH_RANGE and year indicated in YEAR_2

Consumer Price Index CPI in India increased to 193 points in May from 192.60 points in April of 2025. This dataset provides - India Consumer Price Index (CPI) - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Abstract

The Consumer price surveys primarily provide the following: Data on CPI in Palestine covering the West Bank, Gaza Strip and Jerusalem J1 for major and sub groups of expenditure. Statistics needed for decision-makers, planners and those who are interested in the national economy. Contribution to the preparation of quarterly and annual national accounts data.

Consumer Prices and indices are used for a wide range of purposes, the most important of which are as follows: Adjustment of wages, government subsidies and social security benefits to compensate in part or in full for the changes in living costs. To provide an index to measure the price inflation of the entire household sector, which is used to eliminate the inflation impact of the components of the final consumption expenditure of households in national accounts and to dispose of the impact of price changes from income and national groups. Price index numbers are widely used to measure inflation rates and economic recession. Price indices are used by the public as a guide for the family with regard to its budget and its constituent items. Price indices are used to monitor changes in the prices of the goods traded in the market and the consequent position of price trends, market conditions and living costs. However, the price index does not reflect other factors affecting the cost of living, e.g. the quality and quantity of purchased goods. Therefore, it is only one of many indicators used to assess living costs. It is used as a direct method to identify the purchasing power of money, where the purchasing power of money is inversely proportional to the price index.

Geographic coverage

Palestine West Bank Gaza Strip Jerusalem

Analysis unit

The target population for the CPI survey is the shops and retail markets such as grocery stores, supermarkets, clothing shops, restaurants, public service institutions, private schools and doctors.

Universe

The target population for the CPI survey is the shops and retail markets such as grocery stores, supermarkets, clothing shops, restaurants, public service institutions, private schools and doctors.

Kind of data

Sample survey data [ssd]

Sampling procedure

A non-probability purposive sample of sources from which the prices of different goods and services are collected was updated based on the establishment census 2017, in a manner that achieves full coverage of all goods and services that fall within the Palestinian consumer system. These sources were selected based on the availability of the goods within them. It is worth mentioning that the sample of sources was selected from the main cities inside Palestine: Jenin, Tulkarm, Nablus, Qalqiliya, Ramallah, Al-Bireh, Jericho, Jerusalem, Bethlehem, Hebron, Gaza, Jabalia, Dier Al-Balah, Nusseirat, Khan Yunis and Rafah. The selection of these sources was considered to be representative of the variation that can occur in the prices collected from the various sources. The number of goods and services included in the CPI is approximately 730 commodities, whose prices were collected from 3,200 sources. (COICOP) classification is used for consumer data as recommended by the United Nations System of National Accounts (SNA-2008).

Sampling deviation

Not apply

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

A tablet-supported electronic form was designed for price surveys to be used by the field teams in collecting data from different governorates, with the exception of Jerusalem J1. The electronic form is supported with GIS, and GPS mapping technique that allow the field workers to locate the outlets exactly on the map and the administrative staff to manage the field remotely. The electronic questionnaire is divided into a number of screens, namely: First screen: shows the metadata for the data source, governorate name, governorate code, source code, source name, full source address, and phone number. Second screen: shows the source interview result, which is either completed, temporarily paused or permanently closed. It also shows the change activity as incomplete or rejected with the explanation for the reason of rejection. Third screen: shows the item code, item name, item unit, item price, product availability, and reason for unavailability. Fourth screen: checks the price data of the related source and verifies their validity through the auditing rules, which was designed specifically for the price programs. Fifth screen: saves and sends data through (VPN-Connection) and (WI-FI technology).

In case of the Jerusalem J1 Governorate, a paper form has been designed to collect the price data so that the form in the top part contains the metadata of the data source and in the lower section contains the price data for the source collected. After that, the data are entered into the price program database.

Cleaning operations

The price survey forms were already encoded by the project management depending on the specific international statistical classification of each survey. After the researcher collected the price data and sent them electronically, the data was reviewed and audited by the project management. Achievement reports were reviewed on a daily and weekly basis. Also, the detailed price reports at data source levels were checked and reviewed on a daily basis by the project management. If there were any notes, the researcher was consulted in order to verify the data and call the owner in order to correct or confirm the information.

At the end of the data collection process in all governorates, the data will be edited using the following process: Logical revision of prices by comparing the prices of goods and services with others from different sources and other governorates. Whenever a mistake is detected, it should be returned to the field for correction. Mathematical revision of the average prices for items in governorates and the general average in all governorates. Field revision of prices through selecting a sample of the prices collected from the items.

Response rate

Not apply

Sampling error estimates

The findings of the survey may be affected by sampling errors due to the use of samples in conducting the survey rather than total enumeration of the units of the target population, which increases the chances of variances between the actual values we expect to obtain from the data if we had conducted the survey using total enumeration. The computation of differences between the most important key goods showed that the variation of these goods differs due to the specialty of each survey. For example, for the CPI, the variation between its goods was very low, except in some cases such as banana, tomato, and cucumber goods that had a high coefficient of variation during 2019 due to the high oscillation in their prices. The variance of the key goods in the computed and disseminated CPI survey that was carried out on the Palestine level was for reasons related to sample design and variance calculation of different indicators since there was a difficulty in the dissemination of results by governorates due to lack of weights. Non-sampling errors are probable at all stages of data collection or data entry. Non-sampling errors include: Non-response errors: the selected sources demonstrated a significant cooperation with interviewers; so, there wasn't any case of non-response reported during 2019. Response errors (respondent), interviewing errors (interviewer), and data entry errors: to avoid these types of errors and reduce their effect to a minimum, project managers adopted a number of procedures, including the following: More than one visit was made to every source to explain the objectives of the survey and emphasize the confidentiality of the data. The visits to data sources contributed to empowering relations, cooperation, and the verification of data accuracy. Interviewer errors: a number of procedures were taken to ensure data accuracy throughout the process of field data compilation: Interviewers were selected based on educational qualification, competence, and assessment. Interviewers were trained theoretically and practically on the questionnaire. Meetings were held to remind interviewers of instructions. In addition, explanatory notes were supplied with the surveys. A number of procedures were taken to verify data quality and consistency and ensure data accuracy for the data collected by a questioner throughout processing and data entry (knowing that data collected through paper questionnaires did not exceed 5%): Data entry staff was selected from among specialists in computer programming and were fully trained on the entry programs. Data verification was carried out for 10% of the entered questionnaires to ensure that data entry staff had entered data correctly and in accordance with the provisions of the questionnaire. The result of the verification was consistent with the original data to a degree of 100%. The files of the entered data were received, examined, and reviewed by project managers before findings were extracted. Project managers carried out many checks on data logic and coherence, such as comparing the data of the current month with that of the previous month, and comparing the data of sources and between governorates. Data collected by tablet devices were checked for consistency and accuracy by applying rules at item level to be checked.

Data appraisal

Other technical procedures to improve data quality: Seasonal adjustment processes

Inflation Rate in India decreased to 2.82 percent in May from 3.16 percent in April of 2025. This dataset provides - India Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Core consumer prices in the United States increased 2.80 percent in May of 2025 over the same month in the previous year. This dataset provides - United States Core Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

📈 Daily Historical Stock Price Data for CPI Property Group (2006–2025)

A clean, ready-to-use dataset containing daily stock prices for CPI Property Group from 2006-05-25 to 2025-05-28. This dataset is ideal for use in financial analysis, algorithmic trading, machine learning, and academic research.

  🗂️ Dataset Overview

Company: CPI Property Group Ticker Symbol: O5G.DE Date Range: 2006-05-25 to 2025-05-28 Frequency: Daily Total Records: 4828 rows (one per trading day)… See the full description on the dataset page: https://huggingface.co/datasets/khaledxbenali/daily-historical-stock-price-data-for-cpi-property-group-20062025.

The dataset represents the joint dynamics of Financial Stress Index (FSI), Consumer Price Index (CPI) calculated and provided by the National Bank of Ukraine (NBU) and Gross Domestic Product (GDP) provided by SSSU for Ukraine.

The monthly dataset range is Feb 2004-Feb 2022, the effective balanced range is Jan 2011-Dec 2021.

The daily FSI data is aggregated into monthly series as a period average. The CPI series are monthly. The quarterly GDP data is seasonally adjusted and interpolated into monthly data with the use of ARIMA model and cubic spline method accordingly, converted into year-over-year series (dGDP).

Inflation Rate in Brazil decreased to 5.32 percent in May from 5.53 percent in April of 2025. This dataset provides - Brazil Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

What happens to gold if CPI increases?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Monthly indexes and percentage changes for all components and special aggregates of the Consumer Price Index (CPI), not seasonally adjusted, for Canada, provinces, Whitehorse, Yellowknife and Iqaluit. Data are presented for the corresponding month of the previous year, the previous month and the current month. The base year for the index is 2002=100.

The Three-View Cloud Particle Imager (3V-CPI) is a combination of three imaging instruments. Two of them comprise a 2D-S instrument, in which two 2D probes image particles as they pass through beams that are oriented orthogonally to each other and the airflow. If particles also lie in the intersection of the sensitive areas of the two beams, they are seen by both 2D probes. In that case, the CPI is triggered to take a high-resolution picture, via a briefly illuminated high-resolution imaging array, to provide a third image at high resolution. The probe is particularly suited to imaging ice crystals, but also provide good detection of other hydrometeors including large cloud droplets, drizzle and small rain drops, and precipitation particles. The 3V-CPI measures the size, shape and concentration of water drops and ice particles in clouds in the size range of 15-250 micrometers. This dataset contains 2D-S imagery collected by the 3V-CPI aboard the NSF/NCAR C-130 during the ICE-T project. These data have been converted to a format compatible with xpms2d, available from the EOL xpms2d download page.

Consumer Price Index CPI in Saudi Arabia increased to 113.46 points in April from 113.10 points in March of 2025. This dataset provides - Saudi Arabia Consumer Price Index (CPI) - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Consumer Price Index CPI in Ireland remained unchanged at 102.60 points in May. This dataset provides the latest reported value for - Ireland Consumer Price Index (cpi) - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

About this dataset

Context

The dataset illustrates the median household income in Grass Range, spanning the years from 2010 to 2021, with all figures adjusted to 2022 inflation-adjusted dollars. Based on the latest 2017-2021 5-Year Estimates from the American Community Survey, it displays how income varied over the last decade. The dataset can be utilized to gain insights into median household income trends and explore income variations.

Key observations:

From 2010 to 2021, the median household income for Grass Range increased by $2,818 (6.93%), as per the American Community Survey estimates. In comparison, median household income for the United States increased by $4,559 (6.51%) between 2010 and 2021.

Analyzing the trend in median household income between the years 2010 and 2021, spanning 11 annual cycles, we observed that median household income, when adjusted for 2022 inflation using the Consumer Price Index retroactive series (R-CPI-U-RS), experienced growth year by year for 5 years and declined for 6 years.

https://i.neilsberg.com/ch/grass-range-mt-median-household-income-trend.jpeg" alt="Grass Range, MT median household income trend (2010-2021, in 2022 inflation-adjusted dollars)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2022-inflation-adjusted dollars.

Years for which data is available:

• 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021

Variables / Data Columns

• Year: This column presents the data year from 2010 to 2021
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific year
• YOY Change($): Change in median household income between the current and the previous year, in 2022 inflation-adjusted dollars
• YOY Change(%): Percent change in median household income between current and the previous year

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Grass Range median household income. You can refer the same here

description: A coastal change potential (CPI) was used to map the relative change potential of the coast to sea-level change within Kenai Fjords National Park in Alaska. The CPI ranks the following in terms of their physical contribution to sea-level rise-related coastal change: geomorphology, regional coastal slope, rate of relative sea-level change, historical shoreline change rates, mean tidal range and mean significant wave height. The rankings for each input variable were combined and an index value calculated for 1-minute grid cells covering the park. The CPI highlights those regions where the physical effects of sea-level rise might be the greatest. This approach combines the coastal system's susceptibility to change with its natural ability to adapt to changing environmental conditions, yielding a quantitative, although relative, measure of the park's natural change potential to the effects of sea-level change. The CPI and the data contained within this dataset provide an objective technique for evaluation and long-term planning by scientists and park managers.; abstract: A coastal change potential (CPI) was used to map the relative change potential of the coast to sea-level change within Kenai Fjords National Park in Alaska. The CPI ranks the following in terms of their physical contribution to sea-level rise-related coastal change: geomorphology, regional coastal slope, rate of relative sea-level change, historical shoreline change rates, mean tidal range and mean significant wave height. The rankings for each input variable were combined and an index value calculated for 1-minute grid cells covering the park. The CPI highlights those regions where the physical effects of sea-level rise might be the greatest. This approach combines the coastal system's susceptibility to change with its natural ability to adapt to changing environmental conditions, yielding a quantitative, although relative, measure of the park's natural change potential to the effects of sea-level change. The CPI and the data contained within this dataset provide an objective technique for evaluation and long-term planning by scientists and park managers.

📈 Daily Historical Stock Price Data for CPI Europe AG (2005–2025)

A clean, ready-to-use dataset containing daily stock prices for CPI Europe AG from 2005-01-12 to 2025-05-28. This dataset is ideal for use in financial analysis, algorithmic trading, machine learning, and academic research.

  🗂️ Dataset Overview

Company: CPI Europe AG Ticker Symbol: IMO1.F Date Range: 2005-01-12 to 2025-05-28 Frequency: Daily Total Records: 5179 rows (one per trading day)

  🔢… See the full description on the dataset page: https://huggingface.co/datasets/khaledxbenali/daily-historical-stock-price-data-for-cpi-europe-ag-20052025.