FRBOP Forecast: Core CPI Inflation: sa: Mean: Plus 1 Qtr data was reported at 2.226 % in Mar 2019. This records a decrease from the previous number of 2.327 % for Dec 2018. FRBOP Forecast: Core CPI Inflation: sa: Mean: Plus 1 Qtr data is updated quarterly, averaging 1.951 % from Mar 2007 (Median) to Mar 2019, with 49 observations. The data reached an all-time high of 2.365 % in Jun 2018 and a record low of 1.127 % in Mar 2009. FRBOP Forecast: Core CPI Inflation: sa: Mean: Plus 1 Qtr data remains active status in CEIC and is reported by Federal Reserve Bank of Philadelphia. The data is categorized under Global Database’s United States – Table US.I008: Consumer Price Index: Urban: sa: Forecast: Federal Reserve Bank of Philadelphia.

Dive into Market Research Intellect's Index Fund Market Report, valued at USD 5.0 trillion in 2024, and forecast to reach USD 10.0 trillion by 2033, growing at a CAGR of 8.5% from 2026 to 2033.

Consumer Price Index data was reported at 1.340 Index, 2017 in 2026. This records an increase from the previous number of 1.310 Index, 2017 for 2025. Consumer Price Index data is updated yearly, averaging 0.589 Index, 2017 from Dec 1960 (Median) to 2026, with 67 observations. The data reached an all-time high of 1.340 Index, 2017 in 2026 and a record low of 0.121 Index, 2017 in 1960. Consumer Price Index data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s United States – Table US.OECD.EO: Consumer and Wholesale Price Index: Forecast: OECD Member: Annual. CPI-Consumer price indexIndex, national reference year

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.

CAC 40 Index Forecast: Mixed Signals

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

The Global Forecast System (GFS) CPEX dataset includes model data simulated by the Global Forecast System (GFS) model for the Convective Process Experiment (CPEX) field campaign. The NASA Convective Processes Experiment (CPEX) aircraft field campaign took place in the North Atlantic-Gulf of America-Caribbean Sea region from 25 May-25 June 2017. CPEX conducted a total of sixteen DC-8 missions from 27 May-24 June. The CPEX campaign collected data to help explain convective storm initiation, organization, growth, and dissipation in the North Atlantic-Gulf of America-Caribbean Oceanic region during the early summer of 2017. These data are available from May 24, 2017 through July 20, 2017 and are available in netCDF-3 format.

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.

Machine Learning stock prediction: SET Index Stock Prediction

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

Russia MED Forecast: Baseline Scenario: Consumer Price Index (CPI): Non Food excluding Gasoline: Year Average data was reported at 103.981 % in 2026. This records an increase from the previous number of 103.852 % for 2025. Russia MED Forecast: Baseline Scenario: Consumer Price Index (CPI): Non Food excluding Gasoline: Year Average data is updated yearly, averaging 104.785 % from Dec 2020 (Median) to 2026, with 7 observations. The data reached an all-time high of 115.537 % in 2022 and a record low of 103.501 % in 2020. Russia MED Forecast: Baseline Scenario: Consumer Price Index (CPI): Non Food excluding Gasoline: Year Average data remains active status in CEIC and is reported by Ministry of Economic Development of the Russian Federation. The data is categorized under Global Database’s Russian Federation – Table RU.IA027: Consumer Price Index: Forecast: Ministry of Economic Development.

Terrorism Index in Cuba remained unchanged at 0 Points in 2023 from 0 Points in 2022. Cuba Terrorism Index - values, historical data, forecasts and news - updated on July of 2025.

Euro Area's main stock market index, the EU50, rose to 5381 points on July 24, 2025, gaining 0.70% from the previous session. Over the past month, the index has climbed 2.45% and is up 11.84% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Euro Area. Euro Area Stock Market Index (EU50) - values, historical data, forecasts and news - updated on July of 2025.

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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.

MSCI World Index: Global Pathfinder or Market Mirage?

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

Comoros scored 21 points out of 100 on the 2024 Corruption Perceptions Index reported by Transparency International. This dataset provides the latest reported value for - Comoros Corruption Index - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus 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.

IDX Index: The Ultimate Real Estate Search?

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

CZ: Consumer Price Index: Double Hit Scenario data was reported at 1.215 Index, 2010 in 2021. This records an increase from the previous number of 1.199 Index, 2010 for 2020. CZ: Consumer Price Index: Double Hit Scenario data is updated yearly, averaging 0.917 Index, 2010 from Dec 1993 (Median) to 2021, with 29 observations. The data reached an all-time high of 1.215 Index, 2010 in 2021 and a record low of 0.468 Index, 2010 in 1993. CZ: Consumer Price Index: Double Hit Scenario data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Czech Republic – Table CZ.OECD.EO: Consumer and Wholesale Price Index: Forecast: OECD Member: Annual. CPI-Consumer price indexIndex, national reference year

India's main stock market index, the SENSEX, rose to 82727 points on July 23, 2025, gaining 0.66% from the previous session. Over the past month, the index has climbed 0.82% and is up 3.22% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from India. BSE SENSEX Stock Market Index - values, historical data, forecasts and news - updated on July of 2025.

Zew Economic Sentiment Index in Italy increased to 59.70 in March from 56.80 in February of 2021. This dataset provides the latest reported value for - Italy Zew Economic Sentiment Index - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus 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.

Can neural networks predict stock market? (BEL 20 Index Stock Forecast)

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.

Will the FTSE MIB Index Reach New Heights?

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 dataset includes .nc files designed to replicate a toy example for emulating the Fire Weather Index (FWI) using deep learning techniques. Additionally, .h5 files containing 40 years of pre-trained models are provided for FWI emulation.

Predictands:

• yTrain.nc: A 5-year ERA5-Land computed FWI dataset used to train a toy example deep learning model.

• yTest.nc: A 3-year ERA5-Land computed FWI dataset used for prediction.

Predictors:

• t2m_Train.nc: A 5-year ERA5-Land daily mean surface temperature dataset used as a predictor to train a deep learning model.

• hurs_Train.nc: A 5-year ERA5-Land daily mean relative humidity dataset used as a predictor to train a deep learning model.

• sfcwind_Train.nc: A 5-year ERA5-Land daily mean wind speed dataset used as a predictor to train a deep learning model.

Preprocessed Predictors:

• xTest_stand.Rdata: A dataset containing pre-prepared predictor sets for use with a 40-year pre-trained deep learning model.

Deep Learning Models:

• dense_P1.h5: A 40-year pre-trained Fully Connected Dense (FCD) model, stored in HDF5 format.

• unet_P1.h5: A 40-year pre-trained U-Net model, stored in HDF5 format.