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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.
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)
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)
Stock price prediction
Portfolio optimization
Algorithmic trading
Market sentiment analysis
Risk management
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
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
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United States - Producer Price Index by Commodity for Miscellaneous Products: Audio Discs, Full-Length (Including CDs and Vinyl Records) was 104.80000 Index Dec 2010=100 in January of 2019, according to the United States Federal Reserve. Historically, United States - Producer Price Index by Commodity for Miscellaneous Products: Audio Discs, Full-Length (Including CDs and Vinyl Records) reached a record high of 106.40000 in March of 2014 and a record low of 99.20000 in July of 2011. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Producer Price Index by Commodity for Miscellaneous Products: Audio Discs, Full-Length (Including CDs and Vinyl Records) - last updated from the United States Federal Reserve on July of 2025.
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United Kingdom Retail Price Index: Weights: LG: CDs and Tapes data was reported at 2.000 Per 1000 in 2018. This stayed constant from the previous number of 2.000 Per 1000 for 2017. United Kingdom Retail Price Index: Weights: LG: CDs and Tapes data is updated yearly, averaging 5.000 Per 1000 from Dec 1987 (Median) to 2018, with 32 observations. The data reached an all-time high of 10.000 Per 1000 in 2003 and a record low of 2.000 Per 1000 in 2018. United Kingdom Retail Price Index: Weights: LG: CDs and Tapes data remains active status in CEIC and is reported by Office for National Statistics. The data is categorized under Global Database’s UK – Table UK.I012: Retail Price Index: Weights.
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Graph and download economic data for Producer Price Index by Commodity for Miscellaneous Products: Audio Discs, Full-Length (Including CDs and Vinyl Records) (WPU159C01011) from Dec 2010 to Jan 2019 about recording, miscellaneous, commodities, PPI, inflation, price index, indexes, price, and USA.
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Mexico Consumer Price Index (CPI): Food: Meat: CDS: Ham data was reported at 154.983 Jun2002=100 in Dec 2010. This records an increase from the previous number of 154.710 Jun2002=100 for Nov 2010. Mexico Consumer Price Index (CPI): Food: Meat: CDS: Ham data is updated monthly, averaging 35.223 Jun2002=100 from Jan 1973 (Median) to Dec 2010, with 456 observations. The data reached an all-time high of 154.983 Jun2002=100 in Dec 2010 and a record low of 0.063 Jun2002=100 in Jan 1973. Mexico Consumer Price Index (CPI): Food: Meat: CDS: Ham data remains active status in CEIC and is reported by Bank of Mexico. The data is categorized under Global Database’s Mexico – Table MX.I005: Consumer Price Index: 2002=100.
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Mexico Consumer Price Index (CPI): Food: Meat: CDS: Chorizo data was reported at 174.203 Jun2002=100 in Dec 2010. This records an increase from the previous number of 173.542 Jun2002=100 for Nov 2010. Mexico Consumer Price Index (CPI): Food: Meat: CDS: Chorizo data is updated monthly, averaging 51.533 Jun2002=100 from Jan 1980 to Dec 2010, with 372 observations. The data reached an all-time high of 174.203 Jun2002=100 in Dec 2010 and a record low of 0.231 Jun2002=100 in Jan 1980. Mexico Consumer Price Index (CPI): Food: Meat: CDS: Chorizo data remains active status in CEIC and is reported by Bank of Mexico. The data is categorized under Global Database’s Mexico – Table MX.I005: Consumer Price Index: 2002=100.
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Graph and download economic data for Producer Price Index by Industry: Software and Other Prerecorded Compact Disc, Tape, and Record Reproducing: Audio Discs, Full-Length (Including Compact Discs and Vinyl Records) (PCU33461433461421) from Dec 2003 to Jan 2019 about recording, software, PPI, industry, inflation, price index, indexes, price, and USA.
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Excell sheet include 1-year, and 10-years bond prices, stok indices, cds scores, eurobonds of daily data and credit rating grades of 21 emerging countries between 2000 and 2019.
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Mexico Consumer Price Index (CPI): Food: Meat: CDS: Sausage data was reported at 169.405 Jun2002=100 in Dec 2010. This records an increase from the previous number of 168.417 Jun2002=100 for Nov 2010. Mexico Consumer Price Index (CPI): Food: Meat: CDS: Sausage data is updated monthly, averaging 42.430 Jun2002=100 from Jan 1980 (Median) to Dec 2010, with 372 observations. The data reached an all-time high of 169.405 Jun2002=100 in Dec 2010 and a record low of 0.177 Jun2002=100 in Jan 1980. Mexico Consumer Price Index (CPI): Food: Meat: CDS: Sausage data remains active status in CEIC and is reported by Bank of Mexico. The data is categorized under Global Database’s Mexico – Table MX.I005: Consumer Price Index: 2002=100.
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View the spread between a computed option-adjusted index of all BBB-rated bonds and a spot Treasury curve.
VizieR Online Data Catalog: Coma cluster galaxies absorption-line indices(Price J.+, 2011)
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Netherlands Consumer Price Index (CPI): RC: Equipment incl Repair: RM: CDs data was reported at 86.300 2000=100 in Dec 2006. This records a decrease from the previous number of 87.000 2000=100 for Nov 2006. Netherlands Consumer Price Index (CPI): RC: Equipment incl Repair: RM: CDs data is updated monthly, averaging 96.050 2000=100 from Jan 2000 (Median) to Dec 2006, with 84 observations. The data reached an all-time high of 111.700 2000=100 in Mar 2002 and a record low of 81.600 2000=100 in Nov 2005. Netherlands Consumer Price Index (CPI): RC: Equipment incl Repair: RM: CDs data remains active status in CEIC and is reported by Statistics Netherlands. The data is categorized under Global Database’s Netherlands – Table NL.I007: Consumer Price Index: 2000=100.
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Graph and download economic data for ICE BofA US Corporate Index Option-Adjusted Spread (BAMLC0A0CM) from 1996-12-31 to 2025-07-21 about option-adjusted spread, corporate, and USA.
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United Kingdom Retail Price Index: Leisure Goods: CDs and Tapes data was reported at 122.500 13Jan1987=100 in Jun 2018. This records a decrease from the previous number of 123.900 13Jan1987=100 for May 2018. United Kingdom Retail Price Index: Leisure Goods: CDs and Tapes data is updated monthly, averaging 108.100 13Jan1987=100 from Jan 1987 (Median) to Jun 2018, with 378 observations. The data reached an all-time high of 123.900 13Jan1987=100 in May 2018 and a record low of 88.400 13Jan1987=100 in Mar 2009. United Kingdom Retail Price Index: Leisure Goods: CDs and Tapes data remains active status in CEIC and is reported by Office for National Statistics. The data is categorized under Global Database’s UK – Table UK.I011: Retail Price Index.
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Graph and download economic data for ICE BofA BB US High Yield Index Option-Adjusted Spread (BAMLH0A1HYBB) from 1996-12-31 to 2025-07-21 about BB, option-adjusted spread, yield, interest rate, interest, rate, and USA.
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This dataset provides a series of climate indices derived from reanalysis and model simulations data hosted on the Copernicus Climate Data Store (CDS). These indicators describe how climate variability and change of essential climate variables can impact sectors such as health, agriculture, forestry, energy, tourism, or water and coastal management. Those indices are relevant for adaptation planning at the European and national level and their development was driven by the European Environment Agency (EEA) to address informational needs of climate change adaptation national initiatives across the EU and partner countries as expressed by user requirements and stakeholder consultation. The indices cover the hazard categories introduced by the IPCC and the European Topic Centre on Climate Change Impacts, Vulnerability and Adaptation (ETC-CCA). They are also made available interactively through CDS Toolbox public visualisation apps on the European Climate Data Explorer hosted on EEA’s Climate-adapt site. The indices are either downloaded from the CDS where available, or calculated through a specific CDS Toolbox workflow. In this way both the calculations and the resulting data are fully traceable. As they come from different datasets the underlying climate data differ in their technical specification (type and number of climate and impact models involved, bias-corrected or not, periods covered etc.). An effort was made in the dataset selection to limit the heterogeneity of the underlying dataset as ideally the indices should come from the same dataset with identical specifications. The indices related to temperature, precipitation and wind (20 out of 30) were calculated from atmospheric variables in the same datasets: 'Climate and energy indicators for Europe from 2005 to 2100 derived from climate projections', and 'ERA5 hourly data on single levels from 1940 to present'. The other indices are directly available from CDS datasets generated by specific theme projects. More information about this dataset can be found in the documentation. The underlying datasets hosted on the CDS are:
ERA5 hourly data on single levels from 1940 to present - used to calculate most of the temperature, precipitation and wind speed indicators as it provides the historical and observation based baseline used to monitor the indicators. Climate and energy indicators for Europe from 2005 to 2100 derived from climate projections - used to calculate most of the temperature, precipitation and wind speed indicators as it provides bias-corrected sub-daily data. It is used for all the indicators except those specified in the following datasets below. Fire danger indicators for Europe from 1970 to 2098 derived from climate projections - provides the high fire danger days and fire weather indicators. Hydrology-related climate impact indicators from 1970 to 2100 derived from bias adjusted European climate projections - provides the river flood, river discharge, aridity actual, and mean soil moisture indicators. Mountain tourism meteorological and snow indicators for Europe from 1950 to 2100 derived from reanalysis and climate projections - provides the snowfall amount index. Water level change indicators for the European coast from 1977 to 2100 derived from climate projections - provides the relative sea level rise and extreme sea level indicators.
This dataset was produced on behalf of the Copernicus Climate Change Service.
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This catalogue entry provides the gridded climate data (monthly/annual timeseries) used for the Copernicus Climate Change Service Atlas (C3S Atlas). The gridded datasets consist of in-situ and satellite observation-based datasets, reanalyses (CERRA, ERA5, ERA5-Land, and ORAS5) and global (CMIP5 and CMIP6) and regional (CORDEX) climate projections for the variables and indices included in the C3S Atlas. This dataset complements the Gridded monthly climate projection dataset underpinning the IPCC AR6 Interactive Atlas (IPCC Atlas dataset hereafter), including new datasets, variables and indices. The variables and indices describe various types of climatic impact characteristics: heat and cold, wet and dry, snow and ice, wind and radiation, ocean, circulation and drought characteristics of the climate system. All data sources included in this entry are available in the Climate Data Store (CDS, see “Related data” in the sidebar). Contrary to the frozen IPCC Atlas dataset, this entry will update adding new data on a regular basis. This dataset includes gridded information with monthly/annual temporal resolution for observations/reanalyses of the recent past and climate projections for the 35 variables and indices computed from daily/monthly data across the different datasets. The climate projections are based on Representative Concentration Pathways (RCP) / Shared Socioeconomic Pathways (SSP) scenarios. The datasets are harmonised using regular latitude-longitude grids. Bias correction is available for threshold-based indices. Two methods are available, depending on the variable; linear scaling and the ISIMIP method. This dataset allows the reproduction, expansion and customisation of the climate change products provided interactively by the Copernicus Interactive Climate Atlas. This is an interactive web application displaying global/regional maps of observed trends and climate changes for future periods across scenarios or for global warming levels, and regionally aggregated time series, seasonal cycle plots and climate stripes.
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The prevalent rates of the components of MS by gender (according to the IDF, ATPⅢ, and CDS criteria).
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Mexico Consumer Price Index (CPI): Food: Meat: CDS: Others data was reported at 182.331 Jun2002=100 in Dec 2010. This records an increase from the previous number of 180.630 Jun2002=100 for Nov 2010. Mexico Consumer Price Index (CPI): Food: Meat: CDS: Others data is updated monthly, averaging 44.942 Jun2002=100 from Jan 1980 (Median) to Dec 2010, with 372 observations. The data reached an all-time high of 182.331 Jun2002=100 in Dec 2010 and a record low of 0.208 Jun2002=100 in Sep 1980. Mexico Consumer Price Index (CPI): Food: Meat: CDS: Others data remains active status in CEIC and is reported by Bank of Mexico. The data is categorized under Global Database’s Mexico – Table MX.I005: Consumer Price Index: 2002=100.
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These is a query db representing evaluation queries at: http://www.trec-cds.org/2017.html for use in OWIM
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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.
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)
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)
Stock price prediction
Portfolio optimization
Algorithmic trading
Market sentiment analysis
Risk management
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
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