Germany's main stock market index, the DE40, fell to 24241 points on October 10, 2025, losing 1.50% from the previous session. Over the past month, the index has climbed 2.27% and is up 25.12% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Germany. Germany Stock Market Index (DE40) - values, historical data, forecasts and news - updated on October of 2025.

Germany's main stock market index, the DE40, rose to 24401 points on October 2, 2025, gaining 1.19% from the previous session. Over the past month, the index has climbed 3.42% and is up 28.32% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Germany. Germany Stock Market Index (DE40) - values, historical data, forecasts and news - updated on October of 2025.

Forecast: Import of Woodfree Fine Paper Weighing Less Than 40 g/m2 to Germany 2024 - 2028 Discover more data with ReportLinker!

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.

Buy, sell or hold: CAC 40 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.

[Video] CAC 40: A barometer of French economic health?

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

France's main stock market index, the FR40, fell to 7918 points on October 10, 2025, losing 1.53% from the previous session. Over the past month, the index has climbed 1.21% and is up 4.49% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from France. France Stock Market Index (FR40) - values, historical data, forecasts and news - updated on October of 2025.

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

Snapshot img

Weather Forecasting Services Market Size 2025-2029

The weather forecasting services market size is valued to increase USD 1.6 billion, at a CAGR of 11.8% from 2024 to 2029. Farmers need weather forecasting services will drive the weather forecasting services market.

Major Market Trends & Insights

APAC dominated the market and accounted for a 40% growth during the forecast period.
By Type - Medium-range segment was valued at USD 555.80 billion in 2023
By Application - Energy and utilities segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 148.85 million
Market Future Opportunities: USD 1595.10 million
CAGR : 11.8%
APAC: Largest market in 2023

Market Summary

The market encompasses a dynamic and essential industry, driven by advancements in core technologies and applications. With the increasing reliance on accurate weather information for various sectors, such as agriculture and renewable energy production, the market's significance continues to grow. For instance, farmers heavily depend on weather forecasting services to optimize crop yields and mitigate potential losses. Moreover, the upsurge in the production of renewable energy necessitates precise weather predictions to ensure efficient energy generation. However, the complexities of weather forecasting models pose significant challenges. These models must account for numerous variables and continually adapt to evolving weather patterns.
One of the major drivers for the market's growth is the increasing adoption of advanced technologies like Artificial Intelligence (AI) and Machine Learning (ML) to improve forecasting accuracy. As of 2021, AI and ML technologies are estimated to account for over 20% of the market share. Despite these advancements, regulatory frameworks and data privacy concerns pose challenges for market growth. Additionally, regional differences in weather patterns and climatic conditions create diverse market opportunities. As the market continues to evolve, stakeholders must navigate these challenges and capitalize on emerging opportunities to remain competitive.

What will be the Size of the Weather Forecasting Services Market during the forecast period?

Get Key Insights on Market Forecast (PDF) Request Free Sample

How is the Weather Forecasting Services Market Segmented and what are the key trends of market segmentation?

The weather forecasting services industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Type

  Medium-range
  Long-range
  Short-range
  Nowcasting


Application

  Energy and utilities
  Aviation
  Media and consumer
  Logistics and transportation
  Others


Method

  Ground-based
  Satellite-based
  Model-based


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan
    South Korea


  Rest of World (ROW)

By Type Insights

The medium-range segment is estimated to witness significant growth during the forecast period.

Weather forecasting services play a crucial role in various sectors, including aviation, agriculture, energy, and finance. Ensemble prediction systems analyze multiple forecasts to enhance accuracy, while aviation weather briefings ensure safe flights. Forecasting model validation ensures reliability, and climate prediction systems help understand long-term trends. Weather risk management mitigates potential losses, and air quality forecasting protects public health. Atmospheric data assimilation combines observations and models, and atmospheric circulation patterns provide context. Weather station networks collect essential data, and severe weather warnings save lives. Weather model ensembles offer probabilistic forecasts, and satellite meteorology provides global coverage. UV index prediction safeguards outdoor activities, and weather prediction accuracy depends on data quality control.

Wind energy forecasting optimizes production, and climate change impacts require adaptation strategies. Marine weather forecasts ensure safe maritime travel, and hydrological forecasting manages water resources. Climate modeling techniques explore future scenarios, high-resolution forecasting enhances precision, and agricultural weather services optimize crop yields. Radar meteorology monitors precipitation, numerical weather prediction models simulate weather, and short-range forecasts provide immediate insights. Geospatial weather data offers location-specific information, and extreme weather events require robust response plans. Model output statistics inform decision-making, and long-range forecasting anticipates trends. Mesoscale modeling focuses on local weather pat

After three years of growth, the Iraqi market for graphic paper with mechanical fibre content under 10% and of weight 40-150 g/m2 decreased by -9.8% to $60M in 2024. In general, consumption, however, continues to indicate a strong increase. Consumption of peaked at $67M in 2023, and then reduced in the following 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 Outlook: Analysts Bullish on the French Stock Market Index

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

Italy's main stock market index, the IT40, fell to 41767 points on October 10, 2025, losing 2.38% from the previous session. Over the past month, the index has declined 1.57%, though it remains 21.74% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from Italy. Italy Stock Market Index (IT40) - values, historical data, forecasts and news - updated on October of 2025.

The FXDL40 TTAAii Data Designators decode as: T1 (F): Forecast T1T2 (FX): Miscellaneous A1A2 (DL): Germany (The bulletin collects reports from stations: EDDM;MUNICH INT ;) (Remarks from Volume-C: GLIDER FORECAST FOR EASTERN PART OF GERMANY SOUTH (IN GERMAN))

ECMWF The new reanalysis project ERA-40 will cover the period from mid-1957 to 2001 including the earlier ECMWF reanalysis ERA-15, 1979-1993. The main objective is to promote the use of global analyses of the state of the atmosphere, land and surface conditions over the period. These datasets contain 6H time resolution surface data for the 6H forecast.

Forecast: Sold Production of Paper Sacks and Bags with a Base Width More than 40 Cm in Germany 2023 - 2027 Discover more data with ReportLinker!

The National Water Model (NWM) is a water forecasting model operated by the National Water Center (NWC) of the NOAA National Weather Service. The NWM continually forecasts flows on 2.7 million stream reaches covering 3.2 million miles of streams and rivers in the continental United States [1]. It operates as part of the national weather forecasting system, with inputs from NOAA numerical weather prediction models, and from weather and water conditions observed through the US Geological Survey's National Water Information System. Reference materials for the computational framework behind NWM is published by NCAR [9] [10].

The NWC generates NWM streamflow forecasts for the continental US (CONUS) with multiple forecast horizons and time steps. Due to the output file sizes, these are normally not available for download more than a couple days at a time [2]. However, for a time a 40-day rolling window of these forecasts was maintained by HydroShare at RENCI [3], and a complete retrospective (August 2016 to the present) of the NWM Analysis & Assimilation outputs is maintained as well (contact help@cuahsi.org for access).

An archive of all NWM forecasts for the period Aug 18 to Sept 10, 2017 has been compiled at RENCI [4] [5], available as netCDF (.nc) files totaling 8TB. These can be browsed, subsetted, visualized, and downloaded (see [6] [7] [8]). In addition to these output files, we have uploaded to this HydroShare resource the input parameter files needed to re-run the NWM for the Harvey period, or for any time period covered by NWM v1.1 and 1.2 (August 2016 to this publication date in August 2018). These parameter files are also made available at [1].

See README for further details and usage guidance. Please see NOAA contacts listed on [1] for questions about the NWM data contents, structure and formats. Contact help@cuahsi.org if any questions about HydroShare-based tools and data access.

References [1] Overview of the NWM framework and output files [http://water.noaa.gov/about/nwm] [2] Free access to all National Water Model output for the most recent two days [http://water.noaa.gov/about/nwm - scroll down to links under "Downloading Output"] [3] NWM outputs for rolling 40-day window, maintained by HydroShare [link is no longer available] [4] Archived Harvey NWM outputs via RENCI THREDDS server [http://thredds.hydroshare.org/thredds/catalog/nwm/harvey/catalog.html] [link is no longer accessible] [5] RENCI is an Institute at the University of North Carolina at Chapel Hill [6] Live map for National Water Model forecasts [http://water.noaa.gov/map] [7] NWM Forecast Viewer app [no longer available] [8] CUAHSI JupyterHub example scripts for subsetting NWM output files [https://hydroshare.org/resource/3db192783bcb4599bab36d43fc3413db/] [9] WRF-Hydro Overview [https://ral.ucar.edu/projects/wrf_hydro/overview] [10] WRF-Hydro User Guide 2015 [https://ral.ucar.edu/sites/default/files/public/images/project/WRF_Hydro_User_Guide_v3.0.pdf]

DS126.0 represents a dataset implemented and computed by NCAR's Data Support Section, and forms an essential part of efforts undertaken in late 2004, early 2005, to produce an archive of selected segments of ERA-40 on a standard transformation grid. In this case, forty seven ERA-40 monthly mean surface and single level analysis variables were transformed from a reduced N80 Gaussian grid to a 256 by 128 regular Gaussian grid. All fields were transformed using routines from the ECMWF EMOS library, including 10 meter winds which were treated as scalars because of a lack of 10 meter spectral vorticity and divergence. A missing value occurs in the sea surface temperature and sea ice fields to mask grid points occurring over land. Fields formerly archived as whole integers, such as vegetation indices and cloud cover, occur as integers plus a fractional part in the T85 version due to interpolation. Twenty seven ERA-40 monthly mean surface and single level 6-hour forecast variables were transformed from a reduced N80 Gaussian grid to a 256 by 128 regular Gaussian grid. Four of the variables are "instantaneous" variables, and the remaining twenty three variables are "accumulated" over the 6-hour forecast time. Divide the accumulated variables by 21600 seconds to obtain instantaneous values. (Multiplication by minus one may also be necessary to match the sign convention one is accustomed to.) All fields were transformed using routines from the ECMWF EMOS library, including three pairs of stresses which were treated as scalars because of a lack of spectral precursors. In addition, all corresponding 00Z, 06Z, 12Z, and 18Z monthly mean surface and single level analysis variables and 6-hour forecast variables were also transformed to a T85 Gaussian grid. All forecast variables are valid 6 hours after the forecast was initiated. Thus, 00Z 6-hour forecast evaporation is valid at 06Z. Divide the accumulated variables by 21600 seconds to obtain instantaneous values....

The Turkish market for graphic paper with mechanical fibre content under 10% and of weight 40-150 g/m2 dropped modestly to $685M in 2024, which is down by -2.4% against the previous year. The market value increased at an average annual rate of +1.4% from 2012 to 2024; the trend pattern indicated some noticeable fluctuations being recorded in certain years. Over the period under review, the market hit record highs at $806M in 2016; however, from 2017 to 2024, consumption remained at a lower figure.

Monthly means of surface and flux forecast data from ECMWF ERA-40 reanalysis project are in this dataset.

The statistics shows a forecast of population growth in Denmark from 2018 to 2028, by age group (in millions). According to the forecast, all age groups will keep on increasing slightly except the age group of 40 to 59 year olds which is forecasted to decrease from 1.56 million in 2018 to 1.43 million in 2028.

Laneth-40 Market Analysis: Laneth-40, a non-ionic surfactant derived from lanolin, holds a significant presence in the global market, valued at millions of dollars in 2025. With an impressive CAGR, the market is projected to witness substantial growth over the forecast period of 2025-2033. Key drivers include its emulsification and conditioning properties, making it a preferred choice for a range of cosmetic, skin care, and personal care products. Market Trends and Segments: The increasing demand for natural and eco-friendly ingredients in cosmetics and skincare drives the Laneth-40 market. Additionally, the growing awareness of skin health and the desire for products that provide both cleansing and moisturizing benefits contribute to its popularity. Prominent market players like Nikkol, Croda, and Lanolines Stella dominate the industry, with regional markets in North America, Europe, and Asia Pacific offering significant growth potential. Segments based on application (cosmetic, skin care, etc.) and type (98-99%, above 99%) further define the market landscape. Laneth-40, an ethoxylated fatty acid, has gained significant traction in the personal care and cosmetics industry. This report provides a comprehensive overview of the Laneth-40 market, exploring key trends, driving forces, challenges, and growth opportunities.