Coffee fell to 408.66 USd/Lbs on December 2, 2025, down 0.95% from the previous day. Over the past month, Coffee's price has risen 0.50%, and is up 38.54% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Coffee - values, historical data, forecasts and news - updated on December of 2025.

Coffee growers raise two species of coffee bean: Arabica and robusta. The former is more expensive, selling for 2.93 U.S. dollars per kilogram in 2018 and projected to increase in price to 6.9 U.S. dollars in 2027. Robusta, named because it can grow at a wider range of altitudes and temperatures, sold for 1.87 U.S. dollars in 2018, projected to sell at 4.6 U.S. dollars per kilogram in 2027. Coffee production Coffee originally comes from Ethiopia, where a significant portion of coffee production continues to take place. The more popular bean, Arabica, takes its name from the Arabian Empire, when coffee consumption spread throughout the Middle East. After overcoming its ban by the Catholic Church, who saw coffee as an intoxicant from the Muslim world, coffee sales per capita are highest in European countries. Major players Starbucks has shaped the modern coffee culture, capitalizing on the Seattle coffee shop scene. This opened gourmet coffee to a wider market, shifting the global demand from cheaper robusta to better-tasting Arabica varieties. This shift has influenced the world coffee market, prompting companies such as McDonalds to open McCafé stores to cater to the evolving tastes of global consumers.

View monthly updates and historical trends for Coffee Arabica Price. Source: World Bank. Track economic data with YCharts analytics.

The vitamin C prices in the United States for Q2 2024 reached 4150 USD/MT in June. The prices encountered significant fluctuations. The market saw a marked decline as extra trade met with reduced demand, especially from the food, medicinal products, and nutritional supplements industries. Asian competition intensified this decline, further lowering prices, while transportation hurdles and increased freight costs compounded the pressure.

View monthly updates and historical trends for Coffee Robusta Price. Source: World Bank. Track economic data with YCharts analytics.

View monthly updates and historical trends for New York Arabica Coffee Price. Source: International Monetary Fund. Track economic data with YCharts analyt…

Dataset: • Commodity Price Data. Eg. Commodity1_price.csv, Commodity2_price.csv, Commodity3_price.csv • Distance Matrix Data. Eg. Commodity1_matrix.csv, Commodity2_matrix.csv, Commodity3_matrix.csv

Price dataset description: It is a time-series data of prices of a particular perishable, limited consumption good or commodity (let’s say C) reported in markets of a country. • Date: It’s the date commodity C was reported in the respective market. • Market: Market in which commodity C was reported. • State: State in which the corresponding market is situated. • Variety: Variety of commodity C reported. • Grade: Grade of commodity C reported. • Tonnage (Arrival): Tonnage of a crop that arrives at the market • Prices: MinimumPrice, ModalPrice, and MaximumPrice columns are the corresponding prices of commodity C for the date-state-market-variety-grade combination.

The data has also been captured in form of combinatorial explosion matrix form. It contains market-varieties-grade combination as one cell in the matrix.

Distance matrix description: It is a distance matrix of one state-market combination with every other state-market combination in KM. The files have a distance matrix, whose entries a(i,j) represent distance between two statemarkets statemarket[i] and statemarket[j] in KMs.

Problem description: We have prices available reported for commodity C in different state and markets of the country. Our objective is to forecast the price of a commodity for a given date, state, market, variety, and grade.

Data Properties: 1. Time Series Data 2. Multivariate and multidimensional: Data is multivariate because a lot of factors (features) is responsible for the price of products (labels). 3. Super Sparse Data 4. We believe that there exists a very high degree of correlation between the price of one market and prices in another market. 5. We believe that there may be a high correlation between the prices of different varieties of the same good in the same mandi.

The price of Alphabet C shares traded on the Nasdaq stock exchange increased continuously during the period between ************ and ************, when the share price peaked at ****** U.S. dollars. Since then, the price of Alphabet C fluctuated and peaked again at ***** dollars as of the end of ************.

In Q3 2025, North America, the Vitamin C Price Index fell 11.68% quarter-over-quarter, reflecting oversupply and weak demand dynamics. Check detailed insights for Europe and APAC.

The Hepatitis C Virus (HCV) Testing Market is estimated to be valued at USD 0.9 billion in 2025 and is projected to reach USD 1.4 billion by 2035, registering a compound annual growth rate (CAGR) of 5.1% over the forecast period.

Cocoa fell to 5,359.52 USD/T on December 1, 2025, down 0.82% from the previous day. Over the past month, Cocoa's price has fallen 18.29%, and is down 41.61% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Cocoa - values, historical data, forecasts and news - updated on December of 2025.

Citigroup reported $196.09B in Market Capitalization this December of 2025, considering the latest stock price and the number of outstanding shares.Data for Citigroup | C - Market Capitalization including historical, tables and charts were last updated by Trading Economics this last December in 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.

Citigroup (C) Stock: Navigating the Financial Landscape

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

Series Name: Consumer Food Price IndexSeries Code: AG_FPA_CFPIRelease Version: 2020.Q2.G.03 This dataset is the part of the Global SDG Indicator Database compiled through the UN System in preparation for the Secretary-General's annual report on Progress towards the Sustainable Development Goals.Indicator 2.c.1: Indicator of food price anomaliesTarget 2.c: Adopt measures to ensure the proper functioning of food commodity markets and their derivatives and facilitate timely access to market information, including on food reserves, in order to help limit extreme food price volatilityGoal 2: End hunger, achieve food security and improved nutrition and promote sustainable agricultureFor more information on the compilation methodology of this dataset, see https://unstats.un.org/sdgs/metadata/

Orange Juice fell to 147.99 USd/Lbs on December 2, 2025, down 0.38% from the previous day. Over the past month, Orange Juice's price has fallen 15.22%, and is down 71.10% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Orange Juice - values, historical data, forecasts and news - updated on December of 2025.

The global USB Type-C market is valued at USD 33.4 billion in 2025. It is slated to reach USD 139.9 billion by 2035, recording an absolute increase of USD 106.5 billion over the forecast period.

Japan TSE: Consolidated (C): Average: PE Ratio: Prime Market (PM) data was reported at 16.300 Times in Apr 2025. This records an increase from the previous number of 16.200 Times for Mar 2025. Japan TSE: Consolidated (C): Average: PE Ratio: Prime Market (PM) data is updated monthly, averaging 16.200 Times from Apr 2022 (Median) to Apr 2025, with 37 observations. The data reached an all-time high of 20.400 Times in Apr 2022 and a record low of 13.700 Times in Jun 2022. Japan TSE: Consolidated (C): Average: PE Ratio: Prime Market (PM) data remains active status in CEIC and is reported by Japan Exchange Group Inc.. The data is categorized under Global Database’s Japan – Table JP.Z: Tokyo Stock Exchange: Price Earnings Ratio. [COVID-19-IMPACT]

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.

Ready Capital Convertible Preferred (RC-C): Worth a Look?

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.

WFC^C Wells Fargo & Company Depositary Shares each representing a 1/1000th interest in a share of Non-Cumulative Perpetual Class A Preferred Stock Series CC

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