The Consumer Price Index (CPI) is a measure of the average change over time in the prices paid by urban consumers for a market basket of consumer goods and services. Indexes are available for the U.S. and various geographic areas. Average price data for select utility, automotive fuel, and food items are also available. Prices for the goods and services used to calculate the CPI are collected in 75 urban areas throughout the country and from about 23,000 retail and service establishments. Data on rents are collected from about 43,000 landlords or tenants. More information and details about the data provided can be found at http://www.bls.gov/cpi

Der CPI Trimmed-Mean in den Vereinigten Staaten blieb im Mai unverändert bei 3 Prozent. Diese Seite bietet - Vereinigte Staaten CPI Trimmed-Mittelwert - tatsächliche Werte, historische Daten, Prognosen, Diagramme, Statistiken, Wirtschaftskalender und Nachrichten.

16% Trimmed-Mean CPI: 42 years of historical data from 1983 to 2025.

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

The transportation sub-index of the CPI basket in the United States increased to 272.95 points in May of 2025 from 272.50 points in April of 2025. This dataset provides - United States Cpi Transportation- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Annual indexes for major components and special aggregates of the Consumer Price Index (CPI), for Canada, provinces, Whitehorse, Yellowknife and Iqaluit. Data are presented for the last five years. The base year for the index is 2002=100.

This table contains 9 series, with data for years 1949 - 2018 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 item: Canada) Alternative measures (9 items: Measure of core inflation based on a factor model, CPI-common (year-over-year percent change); Measure of core inflation based on a weighted median approach, CPI-median (year-over-year percent change); Measure of core inflation based on a trimmed mean approach, CPI-trim (year-over-year percent change); Consumer Price Index (CPI), all-items excluding eight of the most volatile components as defined by the Bank of Canada and excluding the effect of changes in indirect taxes (2002=100); ...).

CPI seasonally adjusted in the United States increased to 320.58 points in May from 320.32 points in April of 2025. This dataset includes a chart with historical data for the United States CPI seasonally adjusted.

This table contains 15 series, with data for years 1990 - 2018 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 item: Canada); Alternative measures (3 items: Measure of core inflation based on a factor model, CPI-common (year-over-year percent change); Measure of core inflation based on a weighted median approach, CPI-median (year-over-year percent change); Measure of core inflation based on a trimmed mean approach, CPI-trim (year-over-year percent change)); Release (5 items: Current release and previous four releases).

description: A coastal change potential index (CPI) was used to map the relative vulnerability of the coast to future lake-level change within Indiana Dunes National Lakeshore in Indiana. The CPI ranks the following in terms of their physical contribution to lake-level change-related coastal change: geomorphology, regional coastal slope, rate of relative lake-level change, historical shoreline change rates, mean annual ice cover 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 lake-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 succeptibility to the effects of lake-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 index (CPI) was used to map the relative vulnerability of the coast to future lake-level change within Indiana Dunes National Lakeshore in Indiana. The CPI ranks the following in terms of their physical contribution to lake-level change-related coastal change: geomorphology, regional coastal slope, rate of relative lake-level change, historical shoreline change rates, mean annual ice cover 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 lake-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 succeptibility to the effects of lake-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.

Inflation Rate in the United States increased to 2.40 percent in May from 2.30 percent in April of 2025. This dataset provides - United States Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Graph and download economic data for Inflation, consumer prices for the United States (FPCPITOTLZGUSA) from 1960 to 2024 about consumer, CPI, inflation, price index, indexes, price, and USA.

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.

A coastal change potential index (CPI) was used to map the relative change potential of the coast to future lake-level change within Apostle Islands National Lakeshore in Wisconsin. The CPI ranks the following in terms of their physical contribution to lake-level change-related coastal change: geomorphology, regional coastal slope, rate of relative lake-level change, historical shoreline change rates, mean annual ice cover 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 lake-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 succeptibility to the effects of lake-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.

Analysis of ‘Walmart Dataset (Retail)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/rutuspatel/walmart-dataset-retail on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Dataset Description :

This is the historical data that covers sales from 2010-02-05 to 2012-11-01, in the file Walmart_Store_sales. Within this file you will find the following fields:

Store - the store number

Date - the week of sales

Weekly_Sales - sales for the given store

Holiday_Flag - whether the week is a special holiday week 1 – Holiday week 0 – Non-holiday week

Temperature - Temperature on the day of sale

Fuel_Price - Cost of fuel in the region

CPI – Prevailing consumer price index

Unemployment - Prevailing unemployment rate

Holiday Events Super Bowl: 12-Feb-10, 11-Feb-11, 10-Feb-12, 8-Feb-13 Labour Day: 10-Sep-10, 9-Sep-11, 7-Sep-12, 6-Sep-13 Thanksgiving: 26-Nov-10, 25-Nov-11, 23-Nov-12, 29-Nov-13 Christmas: 31-Dec-10, 30-Dec-11, 28-Dec-12, 27-Dec-13

Analysis Tasks

Basic Statistics tasks

1) Which store has maximum sales

2) Which store has maximum standard deviation i.e., the sales vary a lot. Also, find out the coefficient of mean to standard deviation

3) Which store/s has good quarterly growth rate in Q3’2012

4) Some holidays have a negative impact on sales. Find out holidays which have higher sales than the mean sales in non-holiday season for all stores together

5) Provide a monthly and semester view of sales in units and give insights

Statistical Model

For Store 1 – Build prediction models to forecast demand

Linear Regression – Utilize variables like date and restructure dates as 1 for 5 Feb 2010 (starting from the earliest date in order). Hypothesize if CPI, unemployment, and fuel price have any impact on sales.

Change dates into days by creating new variable.

Select the model which gives best accuracy.

--- Original source retains full ownership of the source dataset ---

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

CPI Median in the United States remained unchanged at 3.50 percent in May. This dataset provides - United States CPI Median- actual values, historical data, forecast, chart, statistics, economic calendar 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.

Dataset Description :

This is the historical data that covers sales from 2010-02-05 to 2012-11-01, in the file Walmart_Store_sales. Within this file you will find the following fields:

Store - the store number

Date - the week of sales

Weekly_Sales - sales for the given store

Holiday_Flag - whether the week is a special holiday week 1 – Holiday week 0 – Non-holiday week

Temperature - Temperature on the day of sale

Fuel_Price - Cost of fuel in the region

CPI – Prevailing consumer price index

Unemployment - Prevailing unemployment rate

Holiday Events Super Bowl: 12-Feb-10, 11-Feb-11, 10-Feb-12, 8-Feb-13 Labour Day: 10-Sep-10, 9-Sep-11, 7-Sep-12, 6-Sep-13 Thanksgiving: 26-Nov-10, 25-Nov-11, 23-Nov-12, 29-Nov-13 Christmas: 31-Dec-10, 30-Dec-11, 28-Dec-12, 27-Dec-13

Analysis Tasks

Basic Statistics tasks

1) Which store has maximum sales

2) Which store has maximum standard deviation i.e., the sales vary a lot. Also, find out the coefficient of mean to standard deviation

3) Which store/s has good quarterly growth rate in Q3’2012

4) Some holidays have a negative impact on sales. Find out holidays which have higher sales than the mean sales in non-holiday season for all stores together

5) Provide a monthly and semester view of sales in units and give insights

Statistical Model

For Store 1 – Build prediction models to forecast demand

Linear Regression – Utilize variables like date and restructure dates as 1 for 5 Feb 2010 (starting from the earliest date in order). Hypothesize if CPI, unemployment, and fuel price have any impact on sales.

Change dates into days by creating new variable.

Select the model which gives best accuracy.

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.