From 2023 to 2025, the price of coffee in the United States increased each year compared to the previous year. 2024 stands out in particular, with a price change rate of over ** percent. In comparison, the price of coffee on the U.S. market continued to rise in 2025, but only by about ***** percent.

Producer Prices in Albania increased 0.40 percent in March of 2025 over the same month in the previous year. This dataset provides the latest reported value for - Albania Producer Prices Change - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

In the United States, online apparel prices hit a five-year peak in September 2021, when they registered a ***** percent year-over-year increase. In the country, the prices of apparel products available online have fluctuated over the following months. In March 2025, prices decreased by over **** percent compared to the same month of the prior year.

Turkey TR: Wholesale Price Index: % Change data was reported at 6.470 % in Dec 2013. This records an increase from the previous number of 6.406 % for Sep 2013. Turkey TR: Wholesale Price Index: % Change data is updated quarterly, averaging 48.545 % from Mar 1987 (Median) to Dec 2013, with 108 observations. The data reached an all-time high of 137.650 % in Mar 1995 and a record low of -1.567 % in Jun 2009. Turkey TR: Wholesale Price Index: % Change data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s Turkey – Table TR.IMF.IFS: Consumer and Producer Price Index: Quarterly.

Description

This dataset, titled "Cryptocurrency Market Sentiment & Prediction," is a synthetic collection of real-time crypto market data designed for advanced analysis and predictive modeling. It captures a comprehensive range of features including price movements, social sentiment, news impact, and trading patterns for 10 major cryptocurrencies. Tailored for data scientists and analysts, this dataset is ideal for exploring market volatility, sentiment analysis, and price prediction, particularly in the context of significant events like the Bitcoin halving in 2024 and increasing institutional adoption.

Key Features Overview: - Price Movements: Tracks current prices and 24-hour price change percentages to reflect market dynamics. - Social Sentiment: Measures sentiment scores from social media platforms, ranging from -1 (negative) to 1 (positive), to gauge public perception. - News Sentiment and Impact: Evaluates sentiment from news sources and quantifies their potential impact on market behavior. - Trading Patterns: Includes data on 24-hour trading volumes and market capitalization, crucial for understanding market activity. - Technical Indicators: Features metrics like the Relative Strength Index (RSI), volatility index, and fear/greed index for in-depth technical analysis. - Prediction Confidence: Provides a confidence score for predictive models, aiding in assessing forecast reliability.

Purpose and Applications: - Perfect for machine learning tasks such as price prediction, sentiment-price correlation studies, and volatility classification. - Supports time series analysis for forecasting price movements and identifying volatility clusters. - Valuable for research into the influence of social media and news on cryptocurrency markets, especially during high-impact events.

Dataset Scope: - Covers a simulated 30-day period, offering a snapshot of market behavior under varying conditions. - Focuses on major cryptocurrencies including Bitcoin, Ethereum, Cardano, Solana, and others, ensuring relevance to current market trends.

Dataset Structure Table:

Dataset Statistics Table:

This dataset is a powerful resource for machine learning projects, sentiment analysis, and crypto market research, providing a robust foundation for AI/ML model development and testing.

Consulting Services Price Index, percentage change (COSPI) by services. Quarterly data are available from the second quarter of 2014. The table presents quarter-to-quarter and year-to-year percentage changes for various aggregation levels. The base period for the index is (2014=100).

When deciding on how to estimate future prices, due to influences that are likely to affect a product, we should consider two factors: the expected inflation and the real price change. The rate of real price change allows us to plot a trend line based on time series reflecting existing or past market price, that is, on "facts". Usually, many potential users are not going to use sophisticated forecasting techniques to estimate future prices, preferring to rely on simple approximation techniques. If acceptable time price series is available, then the simplest approach is to evidence a trend line over time that can be extended into the future. This can be done with regression analysis. In working with historical data, we could arrive at a medium-term trend estimate, which excludes the effects of inflation. Although the real price of forest products does not usually vary in an exponential way, the normal practice in investment analyses is often simplified by compounding price using a real price change rate. We can get the annual rate of real price change (r) from a linearized model that allows us to keep the statistical robustness of a linear regression model (with statistics, confidence indicators and tests), but applying the compound rate approach used in mathematics of finance. To do that, the well-known basic formula for compounding Pn=P0 (1+r)^n, where: Pn = estimated price in year n P0= price in year 0 r = annual rate of real price change (the real compound rate) n = number of years from year 0

is transformed into that of a straight line by making a change of variables (linearization).

The proposed method is easy to reproduce and seems more orthodox than apply projections made using a simple straight-line model. Even though the straight-line represents an average variation over the years, from a mathematics of finance approach we should discuss price variation in terms of the annual compound rate. In Figure 1, you can see the differences between these approaches. If we have a clear trend in past real prices and the likelihood of a real price variation, we could make future price assumptions. If you agree with this statement and believe that price trend based on historical patterns is a significative information, then you should use r value gotten from the linearized model here proposed to project the price according to the previous compounding equation, where P0 is any real price calculated through the linearized compounding model (Table I). In Catalonia, most of forest products prices have not kept up with inflation and reflect a declining trend. A few others have just barely kept up with inflation. This is means that, despite moderate growth in nominal terms, the real price of almost all Catalan forest products presents a negative trend. For example, Scots pine sawlogs -the most representative harvested species in Catalonia (the 27% of the total volume yearly logged)- have dropped by an average of almost 2% per year since 1980.

Statistics Netherlands collects monthly data on imports and exports of goods. In this table on imports and exports of goods the change of ownership of the goods is decisive, not whether they crossed the Dutch border. The table comprises index figures and changes in terms of percentage of total imports and exports of goods, broken down by value, price and volume. The indices are based on 2021=100. The changes in terms of percentage are compared with the same period in the previous year.

Data available from: 1995 January

Status of the figures: Annual data from 1995 up to and including 2023 are final. Monthly and quarterly data on 2023, 2024 and 2025 are provisional.

Correction as of July 23th 2025: During the changes on July 11th 2025, wrong data on the importvolume and importprices in 2022 have been made final. The final data have now been corrected.

Changes as of August 14th 2025: Data from June and the second quarter of 2025 have been added. The data from March, April and May have been revised.

Statistics Netherlands has carried out a revision of the national accounts. The Dutch national accounts are recently revised. New statistical sources, methods and concepts are implemented in the national accounts, in order to align the picture of the Dutch economy with all underlying source data and international guidelines for the compilation of the national accounts. This table contains revised data. For further information see section 3.

Import and export figures may be adjusted as new or updated source information from the monthly international trade statistics and producer prices becomes available. In addition, the figures are adjusted retrospectively to fit those of imports and exports of goods in the quarterly National Accounts and the annual National Accounts. A complete revision of the National Accounts is carried out once every five years.

When will new figures be published? Six to seven weeks after the end of the month under review.

According to a 2025 survey, nearly half of consumers in the United States intended to switch to more affordable alternatives of their favorite brands if prices rose due to Trump's proposed tariffs on international goods. Another 17 percent would stop purchasing the product altogether.

Producer Prices in Armenia increased 4.30 percent in July of 2025 over the same month in the previous year. This dataset provides - Armenia Producer Prices Change- actual values, historical data, forecast, chart, statistics, economic calendar and news.

The online price changes for a selection of food and drink products from several large UK retailers. These data are experimental estimates developed to deliver timely indicators to help better understand real time economic activity and social change in the UK.

Producer Prices in China decreased 3.60 percent in July of 2025 over the same month in the previous year. This dataset provides the latest reported value for - China Producer Prices Change - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Producer Prices in Belarus increased 6.80 percent in July of 2025 over the same month in the previous year. This dataset provides - Belarus Producer Prices Change - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The latest annual update of consumer price inflation weights.

Industrial product price index (IPPI), by major product group by North American Product Classification System (NAPCS) 2017 Version 2.0. Monthly data are available from January 1956. The table presents month-over-month and year-over-year percentage changes for various aggregation levels. The base period for the index is (202001=100).

Brazil BR: Wholesale Price Index: % Change over Previous Period data was reported at -10.553 % in 2017. This records a decrease from the previous number of 20.413 % for 2016. Brazil BR: Wholesale Price Index: % Change over Previous Period data is updated yearly, averaging 24.064 % from Dec 1949 (Median) to 2017, with 69 observations. The data reached an all-time high of 2,700.000 % in 1990 and a record low of -10.553 % in 2017. Brazil BR: Wholesale Price Index: % Change over Previous Period data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s Brazil – Table BR.IMF.IFS: Consumer and Producer Price Index: Annual.

Why did the Carbon Black Price Change in July 2025? The Carbon Black Price Index FOB Texas exhibited a mixed trend as of Q2 2025, exhibiting a slight decline during April, followed by moderate recoveries in May and June.

Jordan JO: Producer Price Index: % Change over Previous Period data was reported at 4.115 % in 2017. This records an increase from the previous number of -8.768 % for 2016. Jordan JO: Producer Price Index: % Change over Previous Period data is updated yearly, averaging 4.115 % from Dec 2003 (Median) to 2017, with 15 observations. The data reached an all-time high of 56.184 % in 2008 and a record low of -16.669 % in 2009. Jordan JO: Producer Price Index: % Change over Previous Period data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s Jordan – Table JO.IMF.IFS: Consumer and Producer Price Index: Annual.

For-hire motor carrier freight services price index (FHMCFSPI) by North American Industry Classification System (NAICS). Monthly data are available from February 2007. The table presents month-over-month and year-over-year percentage changes for various aggregation levels. The base period for the index is (2021=100).