Consumer Price Index in Japan

The Consumer Price Index (CPI) measures the monthly change in prices paid by consumers.
The CPI is one of the most popular measures of inflation and deflation. The CPI report uses a different survey methodology, price samples, and index weights.

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Files

This dataset include 4 type of data of the CPI:
1. Middle level classification index
(2022_Japan_CPI_middleLevelClassificationIndex.csv)

1. Composite index excluding imputed rent
   (2022_Japan_CPI_compositeIndexExcludingImputedRent.csv)

2. Price index by item
   (2022_Japan_CPI_priceIndexByItems.csv)

3. Goods and service classification index
   (2022_Japan_CPI_GoodsAndServiceClassificationIndex.csv)

Base year is 2020.

Data source

Japanese Government Statistics Portal

Inflation Rate in Japan increased to 3 percent in October from 2.90 percent in September of 2025. This dataset provides the latest reported value for - Japan Inflation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This dataset provides comprehensive historical consumer price index (CPI) data for more than 60 countries, spanning monthly and annual time series. The CPI measures the overall change in the prices of goods and services purchased by households, making it a key macroeconomic indicator for assessing inflation and purchasing power over time. The dataset includes long series that extend back to the mid-19th century for some countries, with an average length of over 60 years for monthly series. The data is widely used as a deflator for other economic series, such as real effective exchange rates and real residential property prices.

Key Features

• Global Coverage: CPI data for over 60 countries.
• Long Time Series: Annual series dating back to the mid-19th century and monthly series averaging over 60 years in length.
• Deflation Tool: Commonly used as a deflator for other economic indicators.
• Inflation Measurement: Essential for tracking inflation and assessing purchasing power.
• Historical Context: Provides insights into economic trends over extended periods.

Inflation Rate in China increased to 0.20 percent in October from -0.30 percent in September of 2025. This dataset provides - China Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Inflation Rate in Costa Rica decreased by 0.38 percent in October from -1 percent in September of 2025. This dataset provides - Costa Rica Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Nominal GDP is an assessment of economic production in an economy but includes the current prices of goods and services in its calculation. GDP is typically measured as the monetary value of goods and services produced.

**Real gross domestic product **(real GDP for short) is a macroeconomic measure of the value of economic output adjusted for price changes (i.e. inflation or deflation). This adjustment transforms the money-value measure, nominal GDP, into an index for quantity of total output.estions do you want to see answered?

Description

The Consumer Price Index for All Urban Consumers: All Items (CPIAUCSL) is a price index of a basket of goods and services paid by urban consumers. Percent changes in the price index measure the inflation rate between any two time periods. The most common inflation metric is the percent change from one year ago. It can also represent the buying habits of urban consumers. This particular index includes roughly 88 percent of the total population, accounting for wage earners, clerical workers, technical workers, self-employed, short-term workers, unemployed, retirees, and those not in the labor force.

The CPIs are based on prices for food, clothing, shelter, and fuels; transportation fares; service fees (e.g., water and sewer service); and sales taxes. The unadjusted series reflects all factors that may influence a change in prices. However, it can be very useful to look at the seasonally adjusted CPI, which removes the effects of seasonal changes, such as weather, school year, production cycles, and holidays.

The CPI can be used to recognize periods of inflation and deflation. Significant increases in the CPI within a short time frame might indicate a period of inflation, and significant decreases in CPI within a short time frame might indicate a period of deflation. However, because the CPI includes volatile food and oil prices, it might not be a reliable measure of inflationary and deflationary periods. For more accurate detection, the core CPI (CPILFESL) is often used. When using the CPI, please note that it is not applicable to all consumers and should not be used to determine relative living costs. Additionally, the CPI is a statistical measure vulnerable to sampling error since it is based on a sample of prices and not the complete average.

Introduction

The Consumer Price Index (CPI) is a measure that examines the average change in prices paid by urban consumers for a basket of goods and services over time. It serves as an indicator of inflation or deflation within an economy. The CPI takes into account a wide range of products and services commonly purchased by households, such as food, housing, transportation, healthcare, and entertainment. By tracking changes in the prices of these items, the CPI provides valuable insights into the overall cost of living and helps in understanding how the purchasing power of consumers is affected by fluctuations in prices.

Navigating the dataset

• Sector: Rural, Urban, or Rural + Urban
• Year, Month: Year and month of the CPI data
• { ...products }: Normalized cost of different products
• General index: Average normalized index for the month.

Factors Affecting USA National Home Prices:

Overview:

This dataset contains a comprehensive collection of indicators which dictate the housing prices in the United States.

1. US Mortgage Rates:

• The average interest rates on mortgage loans in the United States.
• Used to track the cost of borrowing for housing and its impact on the real estate market.

2. Gross Domestic Product (GDP):

• The total monetary value of all goods and services produced within the United States during a specified period.
• A fundamental measure of economic performance, reflecting the overall economic health and growth trends of the country.

3. Unemployment Rates:

• The percentage of the labor force that is currently unemployed and actively seeking employment.
• A crucial indicator of labor market health and economic stability, influencing government policies and social welfare programs.

4. FED Funds Rate:

• The interest rate at which depository institutions lend reserve balances to other depository institutions overnight, as set by the Federal Reserve.
• This rate is a primary tool for monetary policy, influencing borrowing costs and, subsequently, overall economic activity.

5. Population Growth:

• The annual rate at which the U.S. population is changing, reflecting births, deaths, and migration.
• Offers insights into demographic trends, which have implications for labor force, consumer markets, and social services planning.

6. Consumer Price Index (CPI):

• A measure of the average change over time in the prices paid by urban consumers for a market basket of consumer goods and services.
• A key indicator for assessing inflation or deflation, influencing consumer spending behavior and economic policy decisions.

S&P Case-Shiller Housing Price Index (USA):

• Measures changes in the prices of residential real estate properties over time, offering insight into the health and trends of the housing market in the United States.
• Crucial for assessing the state of the housing market, including property values, trends, and their impact on the broader economy.

This dataset provides statistics on real gross domestic product (GDP) and real GDP per capita for subnational regions. Real values are deflation-adjusted using the Regional Producer Price Index (ROPI), where available.

Data source and definition

Regional gross domestic product data is collected at current prices, in millions of national currency from Eurostat (reg_eco10) for EU countries and via delegates of the OECD Working Party on Territorial Indicators (WPTI), as well as from national statistical offices' websites.

To allow comparability over time and between countries, data at current prices are transformed into constant prices and purchasing power parity measures. Regional GDP per capita is calculated by dividing regional GDP by the average annual population of the region.

See method and detailed data sources in Regions and Cities at a Glance 2024, Annex.

Definition of regions

Regions are subnational units below national boundaries. OECD countries have two regional levels: large regions (territorial level 2 or TL2) and small regions (territorial level 3 or TL3). The OECD regions are presented in the OECD Territorial grid (pdf) and in the OECD Territorial correspondence table (xlsx).

Use of economic data on small regions

When economic analyses are carried out at the TL3 level, it is advisable to aggregate data at the metropolitan region level when several TL3 regions are associated to the same metropolitan region. Metropolitan regions combine TL3 regions when 50% or more of the regional population live in a functionnal urban areas above 250 000 inhabitants. This approach corrects the distortions created by commuting. Correspondence between TL3 and metropolitan regions:(xlsx).

Small regions (TL3) are categorized based on shared characteristics into regional typologies. See the economic indicators aggregated by territorial typology at country level on the access to City typology (link) and by urban-rural typology (link).

Cite this dataset

OECD Regions and Cities databases http://oe.cd/geostats

Further information

• The interactive web-tool OECD Regions and Cities Statistical Atlas
• The report OECD Regions and Cities at a Glance

Contact: RegionStat@oecd.org

Inflation Rate in Switzerland decreased to 0.10 percent in October from 0.20 percent in September of 2025. This dataset provides - Switzerland Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Core consumer prices in Japan increased 3 percent in October of 2025 over the same month in the previous year. This dataset provides - Japan Core Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

International Financial Statistics (IFS) is a standard source of international statistics on all aspects of international and domestic finance. It reports, for most countries of the world, current data needed in the analysis of problems of international payments and of inflation and deflation, i.e., data on exchange rates, international liquidity, international banking, money and banking, interest rates, prices, production, international transactions, government accounts, and national accounts. Last update in UNdata: 14 May 2010 If you need more current data, the IMF has made their current database available for bulk download for personal use.

Acknowledgements

This dataset was kindly published by the United Nations on the UNData site. You can find the original dataset here.

License

Per the UNData terms of use: all data and metadata provided on UNdata’s website are available free of charge and may be copied freely, duplicated and further distributed provided that UNdata is cited as the reference.

About Dataset: Walmart Sales Forecast

This dataset focuses on predicting weekly store sales at Walmart by examining holiday effects, temporal patterns, and other influential factors. The goal is to enable efficient stock planning, revenue calculations, and strategic decision-making by understanding patterns related to seasonal sales fluctuations. This machine learning model is developed based on resources from : https://www.kaggle.com/c/walmart-recruiting-store-sales-forecasting/overview/evaluation .

Dataset Overview

1. Test Data Contains 115,064 rows with information: Store, Department, Date, IsHoliday. "IsHoliday" indicates whether the week includes a special holiday. Holidays tend to show higher average sales than non-holiday periods.

2. Train Data Also contains 115,064 rows with Store, Department, Date, IsHoliday, Weekly Sales. Weekly sales are the recorded weekly sales for specific departments at certain stores.

3. Features Data Consists of 8,190 rows with variables such as Temperature, Fuel Price, CPI, Unemployment, Markdown 1-5, IsHoliday * Temperature: Average temperature (Fahrenheit) in a region. * Fuel Price: Can impact consumer spending and sales. * Markdowns 1-5: Promotional markdowns (missing values marked as NA). * CPI: Consumer Price Index (reflects inflation/deflation). * Unemployment: Unemployment rate in a region that affects consumer spending.

4.Store Data Includes details about Walmart stores such as store numbers, store types, and store sizes. Walmart has 45 stores categorized into 3 types: * Type A: Sizes from 39.690 to 219.622 * Type B: Sizes from 34.875 to 140.167 * Type C: Sizes from 39.690 to 42.988 The target variables for prediction are weekly sales, is holiday, and date. The other features are explored to identify patterns and generate insights to build accurate prediction models.

Modeling Objective

The goal is to predict the impact of holidays on weekly store sales. To achieve this, a Time Series modeling approach was applied using variables such as date, weekly sales, is holiday, lag features, rolling averages, and XGBoost. The evaluation metric used was Weighted Mean Absolute Error (WMAE), which emphasizes periods of higher significance, such as holidays.

Final Model Metrics: * Weighted Mean Absolute Error = 211 * Error rate relative to average weekly sales = ~1.32%.

The low error percentage highlights the model's accuracy in forecasting weekly sales and assessing seasonal fluctuations.

Insights

• The analysis provides actionable insights by identifying holiday effects on sales trends.
• This supports better stock planning, strategic financial planning, and risk management.

📢 Published on : My LinkedIn

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.

This dataset provides statistics on real gross value added by broad 10 activities for regions. Real values are deflation-adjusted using the Regional Producer Price Index (ROPI), where available.

Data source and definition

Regional gross value added data is collected at current prices, in millions of national currency from Eurostat (reg_eco10) for EU countries and via delegates of the OECD Working Party on Territorial Indicators (WPTI), as well as from national statistical offices' websites. In order to allow comparability over time and across countries, data in current prices are transformed into constant prices and PPP measures.

See method and detailed data sources in Regions and Cities at a Glance 2024, Annex.

Definition of regions

Regions are subnational units below national boundaries. OECD countries have two regional levels: large regions (territorial level 2 or TL2) and small regions (territorial level 3 or TL3). The OECD regions are presented in the OECD Territorial grid (pdf) and in the OECD Territorial correspondence table (xlsx).

Use of economic data on small regions

When economic analyses are carried out at the TL3 level, it is advisable to aggregate data at the metropolitan region level when several TL3 regions are associated to the same metropolitan region. Metropolitan regions combine TL3 regions when 50% or more of the regional population live in a functionnal urban areas above 250 000 inhabitants. This approach corrects the distortions created by commuting. Correspondence between TL3 and metropolitan regions:(xlsx).

Small regions (TL3) are categorized based on shared characteristics into regional typologies. See the economic indicators aggregated by territorial typology at country level on the access to City typology (link) and by urban-rural typology (link).

Cite this dataset

OECD Regions and Cities databases http://oe.cd/geostats

Further information

• The interactive web-tool OECD Regions and Cities Statistical Atlas
• The report OECD Regions and Cities at a Glance

Contact: RegionStat@oecd.org

Consumer price index and variation rates according to ECOICOP groups by provinces of Spain and periods:This table publishes the Consumer Price Index (CPI) based on 2016, in addition to its monthly, annual and year-to-date variation rates. Changes in the CPI reflect increases (inflation) and decreases (deflation) in the overall price level. The results are broken down according to the groups established in the European Classification of Individual Consumption by Purpose (ECOICOP), as well as by provinces and months.

This dataset captures the economic value of ecosystem services generated by agroforestry systems in Africa. It is based on a systematic literature review (SLR) as well as focus group discussions conducted in selected countries across Africa. The dataset is composed of 164 variables. Descriptive variables indicate details such as source, country and specific location, tree/crop species, sapling survival rate and spacing between trees. The economic factors like inflation deflation and exchange rate are listed. The actual economic values are then divided into costs and benefits. The total costs are separated into maintenance costs and establishment costs. Total, establishment and maintenance costs as well as total benefits are given in USD 2020, the individual value categories are expressed in the currencies used in the original sources for traceability. The cost variables mainly refer to inputs and labour, while the benefits include variables such as income, yield, tree fruits, and sale of tree products.

This dataset captures the economic value of ecosystem services generated by agroforestry systems in Africa. It is based on a systematic literature review (SLR) as well as focus group discussions conducted in selected countries across Africa. The dataset is composed of 164 variables. Descriptive variables indicate details such as source, country and specific location, tree/crop species, sapling survival rate and spacing between trees. The economic factors like inflation deflation and exchange rate are listed. The actual economic values are then divided into costs and benefits. The total costs are separated into maintenance costs and establishment costs. Total, establishment and maintenance costs as well as total benefits are given in USD 2020, the individual value categories are expressed in the currencies used in the original sources for traceability. The cost variables mainly refer to inputs and labour, while the benefits include variables such as income, yield, tree fruits, and sale of tree products.

An implantable muscle closing prosthesis system, particularly for opening and closing the rectum, characterized in that it has a compression unit, which comprises a compression cuff (1) and a reservoir cuff (2) molded as hollow bodies, and may integrate a bidirectional micropump (4) of arbitrary construction via a support ring (3), the cuffs primarily being subjected to a material compression during the inflation and deflation and communicating with one another via the micropump (4), which may be activated using a separate control unit, or is also manually operable from the outside.