Consumer Price Index CPI in the United States increased to 324.80 points in September from 323.98 points in August 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.

In 2023, the U.S. Consumer Price Index was 309.42, and is projected to increase to 352.27 by 2029. The base period was 1982-84. The monthly CPI for all urban consumers in the U.S. can be accessed here. After a time of high inflation, the U.S. inflation rateis projected fall to two percent by 2027. United States Consumer Price Index ForecastIt is projected that the CPI will continue to rise year over year, reaching 325.6 in 2027. The Consumer Price Index of all urban consumers in previous years was lower, and has risen every year since 1992, except in 2009, when the CPI went from 215.30 in 2008 to 214.54 in 2009. The monthly unadjusted Consumer Price Index was 296.17 for the month of August in 2022. The U.S. CPI measures changes in the price of consumer goods and services purchased by households and is thought to reflect inflation in the U.S. as well as the health of the economy. The U.S. Bureau of Labor Statistics calculates the CPI and defines it as, "a measure of the average change over time in the prices paid by urban consumers for a market basket of consumer goods and services." The BLS records the price of thousands of goods and services month by month. They consider goods and services within eight main categories: food and beverage, housing, apparel, transportation, medical care, recreation, education, and other goods and services. They aggregate the data collected in order to compare how much it would cost a consumer to buy the same market basket of goods and services within one month or one year compared with the previous month or year. Given that the CPI is used to calculate U.S. inflation, the CPI influences the annual adjustments of many financial institutions in the United States, both private and public. Wages, social security payments, and pensions are all affected by the CPI.

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.

CPI Card Group Common Stock Price Predictions

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

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.

LON:CPI CAPITA PLC

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

Inflation Rate in India decreased to 0.25 percent in October from 1.44 percent in September of 2025. This dataset provides - India 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.

CVU CPI Aerostructures Inc. Common Stock

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

Capita reported GBP5.87B in Market Capitalization this December of 2025, considering the latest stock price and the number of outstanding shares.Data for Capita | CPI - Market Capitalization including historical, tables and charts were last updated by Trading Economics this last December in 2025.

Israel Bond Index: TASE: General:(CPI) Consumer Price IndexLinked data was reported at 347.940 31Dec1996=100 in Nov 2018. This records a decrease from the previous number of 350.640 31Dec1996=100 for Oct 2018. Israel Bond Index: TASE: General:(CPI) Consumer Price IndexLinked data is updated monthly, averaging 227.960 31Dec1996=100 from Jan 2000 (Median) to Nov 2018, with 227 observations. The data reached an all-time high of 354.310 31Dec1996=100 in Aug 2018 and a record low of 124.180 31Dec1996=100 in Feb 2000. Israel Bond Index: TASE: General:(CPI) Consumer Price IndexLinked data remains active status in CEIC and is reported by Tel Aviv Stock Exchange. The data is categorized under Global Database’s Israel – Table IL.Z002: Tel Aviv Stock Exchange: Bond Index.

Consumer Price Index CPI in India increased to 197.30 points in October from 197 points in September of 2025. This dataset provides - India Consumer Price Index (CPI) - actual values, historical data, forecast, chart, statistics, economic calendar and news.

U.S. Economic and Financial Dataset

Dataset Description

This dataset combines historical U.S. economic and financial indicators, spanning the last 50 years, to facilitate time series analysis and uncover patterns in macroeconomic trends. It is designed for exploring relationships between interest rates, inflation, economic growth, stock market performance, and industrial production.

Key Features

• Frequency: Monthly
• Time Period: Last 50 years from Nov-24
• Sources:
  • Federal Reserve Economic Data (FRED)
  • Yahoo Finance

Dataset Feature Description

1. Interest Rate (Interest_Rate):

   • The effective federal funds rate, representing the interest rate at which depository institutions trade federal funds overnight.

2. Inflation (Inflation):

   • The Consumer Price Index for All Urban Consumers, an indicator of inflation trends.

3. GDP (GDP):

   • Real GDP measures the inflation-adjusted value of goods and services produced in the U.S.

4. Unemployment Rate (Unemployment):

   • The percentage of the labor force that is unemployed and actively seeking work.

5. Stock Market Performance (S&P500):

   • Monthly average of the adjusted close price, representing stock market trends.

6. Industrial Production (Ind_Prod):

   • A measure of real output in the industrial sector, including manufacturing, mining, and utilities.

Dataset Statistics

1. Total Entries: 599
2. Columns: 6
3. Memory usage: 37.54 kB
4. Data types: float64

Feature Overview

• Columns:
  • Interest_Rate: Monthly Federal Funds Rate (%)
  • Inflation: CPI (All Urban Consumers, Index)
  • GDP: Real GDP (Billions of Chained 2012 Dollars)
  • Unemployment: Unemployment Rate (%)
  • Ind_Prod: Industrial Production Index (2017=100)
  • S&P500: Monthly Average of S&P 500 Adjusted Close Prices

Executive Summary

This project explores the interconnected dynamics of key macroeconomic indicators and financial market trends over the past 50 years, leveraging data from the Federal Reserve Economic Data (FRED) and Yahoo Finance. The dataset integrates critical variables such as the Federal Funds Rate, Inflation (CPI), Real GDP, Unemployment Rate, Industrial Production, and the S&P 500 Index, providing a holistic view of the U.S. economy and financial markets.

The analysis focuses on uncovering relationships between these variables through time-series visualization, correlation analysis, and trend decomposition. Key findings are included in the Insights section. This project serves as a robust resource for understanding long-term economic trends, policy impacts, and market behavior. It is particularly valuable for students, researchers, policymakers, and financial analysts seeking to connect macroeconomic theory with real-world data.

Potential Use Cases

• Economic Analysis: Examine relationships between interest rates, inflation, GDP, and unemployment.
• Stock Market Prediction: Study how macroeconomic indicators influence stock market trends.
• Time Series Modeling: Perform ARIMA, VAR, or other models to forecast economic trends.
• Cyclic Pattern Analysis: Identify how economic shocks and recoveries impact key indicators.

Snap of Power Analysis

https://github.com/user-attachments/assets/1b40e0ca-7d2e-4fbc-8cfd-df3f09e4fdb8">

To ensure sufficient power, the dataset covers last 50 years of monthly data i.e., around 600 entries.

Key Insights derived through EDA, time-series visualization, correlation analysis, and trend decomposition

• Interest Rate and Inflation Dynamics: The interest Rate and inflation exhibit an inverse relationship, especially during periods of aggressive monetary tightening by the Federal Reserve.
• Economic Growth and Market Performance: GDP growth and the S&P 500 Index show a positive correlation, reflecting how market performance often aligns with overall economic health.
• Labor Market and Industrial Output: Unemployment and industrial production demonstrate a strong inverse relationship. Higher industrial output is typically associated with lower unemployment
• Market Behavior During Economic Shocks: The S&P 500 experienced sharp declines during significant crises, such as the 2008 financial crash and the COVID-19 pandemic in 2020. These events also triggered increased unemployment and contractions in GDP, highlighting the interplay between markets and the broader economy.
• Correlation Highlights: S&P 500 and GDP have a strong positive correlation. Interest rates negatively correlate with GDP and inflation, reflecting monetary policy impacts. Unemployment is negatively correlated with industrial production but positively correlated with interest rates.

Link to GitHub Repo

https:/...

Israel Bond Index: TASE: Corporate:(CPI) Consumer Price IndexLinked data was reported at 366.150 31Dec1996=100 in Nov 2018. This records a decrease from the previous number of 368.760 31Dec1996=100 for Oct 2018. Israel Bond Index: TASE: Corporate:(CPI) Consumer Price IndexLinked data is updated monthly, averaging 224.660 31Dec1996=100 from Jan 2000 (Median) to Nov 2018, with 227 observations. The data reached an all-time high of 371.460 31Dec1996=100 in Aug 2018 and a record low of 123.990 31Dec1996=100 in Feb 2000. Israel Bond Index: TASE: Corporate:(CPI) Consumer Price IndexLinked data remains active status in CEIC and is reported by Tel Aviv Stock Exchange. The data is categorized under Global Database’s Israel – Table IL.Z002: Tel Aviv Stock Exchange: Bond Index.

This dataset includes various economic indicators such as stock market performance, inflation rates, GDP, interest rates, employment data, and housing index, all of which are crucial for understanding the state of the economy. By analysing this dataset, one can gain insights into the causes and effects of past recessions in the US, which can inform investment decisions and policy-making.

There are 20 columns and 343 rows spanning 1990-04 to 2022-10

The columns are:

1. Price: Price column refers to the S&P 500 lot price over the years. The S&P 500 is a stock market index that measures the performance of 500 large companies listed on stock exchanges in the United States. This variable represents the value of the S&P 500 index from 1980 to present. Industrial Production: This variable measures the output of industrial establishments in the manufacturing, mining, and utilities sectors. It reflects the overall health of the manufacturing industry, which is a key component of the US economy.

2. INDPRO: Industrial production measures the output of the manufacturing, mining, and utility sectors of the economy. It provides insights into the overall health of the economy, as a decline in industrial production can indicate a slowdown in economic activity. This data can be used by policymakers and investors to assess the state of the economy and make informed decisions.

3. CPI: CPI stands for Consumer Price Index, which measures the change in the prices of a basket of goods and services that consumers purchase. CPI inflation represents the rate at which the prices of goods and services in the economy are increasing.

4. Treasure Bill rate (3 month to 30 Years): Treasury bills (T-bills) are short-term debt securities issued by the US government. This variable represents the interest rates on T-bills with maturities ranging from 3 months to 30 years. It reflects the cost of borrowing money for the government and provides an indication of the overall level of interest rates in the economy.

5. GDP: GDP stands for Gross Domestic Product, which is the value of all goods and services produced in a country. This dataset is taking into account only the Nominal GDP values. Nominal GDP represents the total value of goods and services produced in the US economy without accounting for inflation.

6. Rate: The Federal Funds Rate is the interest rate at which depository institutions lend reserve balances to other depository institutions overnight. It is set by the Federal Reserve and is used as a tool to regulate the money supply in the economy.

7. BBK_Index: The BBKI are maintained and produced by the Indiana Business Research Center at the Kelley School of Business at Indiana University. The BBK Coincident and Leading Indexes and Monthly GDP Growth for the U.S. are constructed from a collapsed dynamic factor analysis of a panel of 490 monthly measures of real economic activity and quarterly real GDP growth. The BBK Leading Index is the leading subcomponent of the cycle measured in standard deviation units from trend real GDP growth.

8. Housing Index: This variable represents the value of the housing market in the US. It is calculated based on the prices of homes sold in the market and provides an indication of the overall health of the housing market.

9. Recession binary column: This variable is a binary indicator that takes a value of 1 when the US economy is in a recession and 0 otherwise. It is based on the official business cycle dates provided by the National Bureau of Economic Research.

SENSEX Index (Annual Closing Value) – Benchmark index of the Bombay Stock Exchange (BSE)

GDP Growth (%) – Annual real GDP growth rates (constant prices)

Inflation Rate (%) – Annual consumer price index (CPI)-based inflation

Exchange Rate (INR/USD) – End-of-year nominal exchange rate

Market Capitalization (INR billion) – Total BSE market value

Trading Volume (Million Shares) – Aggregate trading activity per year

All data have been sourced from official publications including the Reserve Bank of India (RBI), BSE archives, International Monetary Fund (IMF), and World Bank.

The dataset is structured in wide format, with each row representing a calendar year from 1980 to 2024 and each column representing one variable.

Discover Market Research Intellect's Cpi Film Market Report, worth USD 1.5 billion in 2024 and projected to hit USD 2.8 billion by 2033, registering a CAGR of 8.5% between 2026 and 2033.Gain in-depth knowledge of emerging trends, growth drivers, and leading companies.

Get key insights on Market Research Intellect's Colorless And Transparent Polyimide (CPI) Market Report: valued at USD 450 million in 2024, set to grow steadily to USD 800 million by 2033, recording a CAGR of 7.5%.Examine opportunities driven by end-user demand, R&D progress, and competitive strategies.

Learn more about the Colorless Polyimides (CPI) Flim Market Report by Market Research Intellect, which stood at USD 450 million in 2024 and is forecast to expand to USD 1.2 billion by 2033, growing at a CAGR of 12.5%.Discover how new strategies, rising investments, and top players are shaping the future.

Cover Window CPI Films Market Outlook

According to our latest research, the Global Cover Window CPI Films market size was valued at $1.2 billion in 2024 and is projected to reach $4.9 billion by 2033, expanding at an impressive CAGR of 17.2% during the forecast period of 2025–2033. The primary growth driver for the global Cover Window CPI Films market is the explosive demand for foldable and flexible electronic devices, particularly in the consumer electronics sector. As manufacturers strive for thinner, lighter, and more durable device displays, CPI (Colorless Polyimide) films have emerged as the material of choice for cover windows due to their superior optical clarity, mechanical strength, and flexibility compared to traditional glass. This trend is further propelled by continuous innovation in material science and advancements in manufacturing processes, enabling CPI films to meet the rigorous requirements of next-generation electronic devices.

Regional Outlook

Asia Pacific currently dominates the Cover Window CPI Films market with a market share exceeding 60% in 2024, making it the region with the largest share globally. This dominance is attributed to the region's mature electronics manufacturing ecosystem, robust presence of leading smartphone and display panel manufacturers, and proactive government policies supporting high-tech material innovation. Countries such as South Korea, China, and Japan have become epicenters for CPI film production, benefiting from established supply chains, advanced research infrastructure, and significant investments in R&D. Additionally, the high consumer demand for foldable smartphones and tablets in Asia Pacific further cements its leadership position, with local companies leveraging economies of scale and rapid commercialization of new technologies.

North America is projected to be the fastest-growing region in the Cover Window CPI Films market, with a forecasted CAGR of 19.5% from 2025 to 2033. This rapid growth is driven by a surge in innovation from both established electronics giants and emerging startups, particularly in the fields of wearable devices, automotive displays, and flexible laptops. The region’s strong focus on technological advancement, coupled with substantial venture capital investments and strategic partnerships, is fostering a dynamic environment for CPI film market expansion. Furthermore, North American consumers are early adopters of cutting-edge electronic devices, and regional manufacturers are increasingly integrating CPI films into their product designs to enhance durability and user experience.

Emerging economies in Latin America, the Middle East, and Africa are gradually entering the Cover Window CPI Films market, although adoption rates remain comparatively lower due to challenges such as limited local manufacturing capacity, higher import tariffs, and a lack of specialized technical expertise. However, as global supply chains become more integrated and multinational electronics brands expand their footprint in these regions, localized demand for advanced display materials is expected to rise. Policy reforms aimed at attracting foreign direct investment in high-tech manufacturing and increasing consumer awareness about the benefits of flexible and durable devices are likely to accelerate market penetration over the coming years, albeit at a more moderate pace compared to Asia Pacific and North America.

Report Scope

Cover Window CPI Films Market Outlook

According to our latest research, the global Cover Window CPI Films market size reached USD 1.08 billion in 2024, reflecting robust demand across consumer electronics and automotive applications. The market is expected to grow at a CAGR of 18.7% from 2025 to 2033, with the forecasted market size projected to reach USD 5.89 billion by 2033. This dynamic growth is primarily driven by the rapid adoption of flexible and foldable display technologies, expanding the use cases for CPI (Colorless Polyimide) films in next-generation device manufacturing.

A key growth driver for the Cover Window CPI Films market is the surging popularity of foldable and flexible smartphones and tablets. Major consumer electronics manufacturers are increasingly integrating CPI films as a core material for cover windows due to their unique combination of optical clarity, flexibility, and durability. Unlike traditional glass, CPI films offer superior resistance to cracking and can be seamlessly integrated into bendable or rollable displays, making them indispensable for the evolving form factors in the electronics industry. As consumer preferences shift towards innovative, compact, and portable devices, the demand for advanced CPI films is expected to remain strong, further fueling market expansion.

Another significant factor contributing to market growth is the ongoing advancements in CPI film manufacturing technologies. Companies are investing heavily in research and development to enhance the mechanical, optical, and barrier properties of CPI films. Innovations such as improved scratch resistance, anti-fingerprint coatings, and enhanced transparency are making these films more suitable for high-end applications, including premium smartphones, automotive displays, and wearable devices. The growing focus on sustainability and recyclability is also prompting manufacturers to develop eco-friendly CPI film variants, thereby broadening the market’s appeal and application scope.

The expanding application of Cover Window CPI Films in the automotive sector represents an additional growth avenue. Modern vehicles are increasingly equipped with advanced infotainment systems, digital instrument clusters, and touch-enabled control panels, all of which require high-performance cover windows. CPI films, with their lightweight and flexible nature, offer automotive OEMs the ability to design curved or non-traditional display shapes, enhancing both aesthetics and functionality. This trend, coupled with the rising integration of smart and connected car technologies, is anticipated to drive substantial demand for CPI films in the automotive industry over the forecast period.

Regionally, the Asia Pacific market continues to dominate the global landscape, accounting for the largest share in 2024, driven by the presence of major electronics manufacturing hubs in China, South Korea, and Japan. North America and Europe are also witnessing significant growth, propelled by the adoption of advanced automotive and industrial display solutions. Latin America and the Middle East & Africa, while smaller in market size, are poised for accelerated growth due to increasing investments in consumer electronics and automotive sectors. The regional dynamics underscore the global appeal and versatility of Cover Window CPI Films, setting the stage for continued expansion across diverse end-use industries.

Product Type Analysis

The Cover Window CPI Films market is segmented by product type into Transparent CPI Films, Colored CPI Films, and Others. Transparent CPI Films currently hold the largest market share, owing to their exceptional clarity, high light transmittance, and minimal color distortion, making them ideal for high-end electronic displays. These films are extensively used in foldable smartphones, tablets, and other devices where pristine image quality and touch sensitivity are paramount. The demand for transparent variants is further bolstered by the ongoing trend towards bezel-less and edge-to-edge displays, which require materials that d

In November 2024, the inflation rate in Turkey corresponded to **** percent. The monthly inflation rate in Turkey reached ***** percent in October 2022, the highest inflation rate recorded during the provided time interval. In June 2023, the year-on-year change in the Consumer Price Index (CPI) was recorded at ***** percent, the lowest since January 2022. Since the second half of 2019, Turkey’s inflation rate has consistently been in double digits, with inflation accelerating at the fastest rate in 2022. High production costs In Turkey, domestic producer price indices have been continuously rising, which has directly resulted in a price increase in all consumer goods and services. Accordingly, the Consumer Price Index (CPI) in all commodity groups increased extremely since 2022. In the same year, the food and non-alcoholic beverages category had one of the highest inflation rates in the CPI. This particularly affected Turkish consumers, as these products accounted for the highest share of household expenditure in 2023. Soaring food prices Since 2020, food prices have increased significantly around the world, and Turkey is no exception. Although inflation has started to slow down recently, food prices in Turkey continue to go up steadily, increasing by **** percent in November 2024 compared to the same month in the previous year. It is not surprising that food inflation has not simmered down, as the producer price index (PPI) of agricultural products followed a constant increasing trend in the country over the past few years.

Consumer Price Index CPI in Pakistan increased to 281.78 points in November from 280.66 points in October of 2025. This dataset provides - Pakistan Consumer Price Index (CPI) - actual values, historical data, forecast, chart, statistics, economic calendar and news.