Time series of major commodity prices and indices including iron, cooper, wheat, gold, oil. Data comes from the International Monetary Fund (IMF).All rights are reservedDataDataset contains Monthly prices for 53 commodities and 10 indexes, starting from 1980 to 2016, Last updated on march 17, 2016. The reference year for indexes are 2005 (meaning the value of indexes are 100 and all other values are relative to that year).LicenseThe IMF grants permission to visit its Sites and to download and copy information, documents, and materials from the Sites for personal, noncommercial usage only, without any right to resell or redistribute or to compile or create derivative works, subject to these Terms and Conditions of Usage and also subject to more specific restrictions that may apply to particular information within the Sites. Any rights not expressly granted herein are reserved.For more information please visit: Copyright and Usage.

Graph and download economic data for Global Price Index of All Commodities (PALLFNFINDEXQ) from Q1 2003 to Q2 2025 about World, commodities, price index, indexes, and price.

Graph and download economic data for Global Price Index of All Commodities (PALLFNFINDEXM) from Jan 2003 to Jun 2025 about World, commodities, price index, indexes, and price.

The World Bank’s Commodity Price historical data and forecasts are published quarterly, in January, April, July and October. The price forecasts go up to 2030. Topics: Agriculture & Rural Development

GSCI rose to 550.26 Index Points on July 24, 2025, up 0.47% from the previous day. Over the past month, GSCI's price has risen 1.48%, but it is still 0.85% lower than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. GSCI Commodity Index - values, historical data, forecasts and news - updated on July of 2025.

CRB Index fell to 375.35 Index Points on July 21, 2025, down 0.40% from the previous day. Over the past month, CRB Index's price has risen 0.25%, and is up 12.61% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. CRB Commodity Index - values, historical data, forecasts and news - updated on July of 2025.

Real-time commodities pricing data allows you to grasp where the market is, was and will be – from exchange data and OTC prices to specialist fundamentals.

Nominal prices in USD for selected key international commodity prices relevant to Pacific Island Countries and Territories, extracted from World bank Commodity Prices (« pink sheets ») and from FAO GLOBEFISH European Fish Price Report.

Find more Pacific data on PDH.stat.

This statistic depicts global commodity price indexes for energy, metal, and agriculture from January 2018 to November 2019. In November 2019, the commodity index for energy stood at 87.7, compared to 86.1 for metals, and 98.4 for agriculture.

The balanced annual panel data for 32 sub-Saharan countries from 2000 to 2020 was used for this study. The countries and period of study was informed by availability of data of interest. Specifically, 11 agricultural commodity dependent countries, 7 energy commodity dependent countries and 14 mineral and metal ore dependent countries were selected (Appendix 1). The annual data comprised of agricultural commodity prices, global oil prices (GOP) and mineral and metal ore prices, export value of the dependent commodity, total export value of the country, real GDP (RGDP) and terms of trade (TOT). The data for export value of the dependent commodity, total export value of the country, real GDP and terms of trade was sourced from world bank database (World Development Indicators). Data for agricultural commodity prices, global oil prices (GOP) and mineral and metal ore prices are obtained from World Bank commodity price data portal. This study used data from global commodity prices from the World Bank's commodity price data site since the error term (endogenous) is connected with each country's commodity export price index. The pricing information covered agricultural products, world oil, minerals, and metal ores. One benefit of adopting international commodity prices, according to Deaton and Miller (1995), is that they are frequently unaffected by national activities. The utilization of studies on global commodity prices is an example (Tahar et al., 2021). The commodity dependency index of country i at time i was computed as the as the ratio of export value of the dependent commodity to the total export value of the country. The commodity price volatility is estimated using standard deviation from monthly commodity price index to incorporate monthly price variation (Aghion et al., 2009). This approach addresses challenges of within the year volatility inherent in the annual data. In footstep of Arezki et al. (2014) and Mondal & Khanam (2018), standard deviation is used in this study as a proxy of commodity price volatility. The standard deviation is used because of its simplicity and it is not conditioned on the unit of measurement.

This dataset aggregates daily wholesale price data for a wide spectrum of agricultural commodities traded across India’s regulated markets (mandis). It captures minimum, maximum, and modal prices, enabling detailed analysis of price dispersion and volatility over time. Data is sourced directly from the AGMARKNET portal and made available under the National Data Sharing and Accessibility Policy (NDSAP). With over 165,000 views and nearly 400,000 downloads, it’s a cornerstone resource for economists, agronomists, and data scientists studying India’s commodity markets.

This dataset provides daily wholesale minimum, maximum, and modal prices for a wide variety of agricultural commodities across India’s mandis, sourced from the AGMARKNET portal and published on Data.gov.in under NDSAP, with records dating back to 2013 and updated as of 19 May 2025 via a REST API; it includes key fields like Arrival_Date, State, District, Market, Commodity, Variety, Min_Price, Max_Price, and Modal_Price, making it ideal for time-series analysis, price-trend visualizations, and commodity forecasting.

Corn rose to 399.78 USd/BU on July 23, 2025, up 0.13% from the previous day. Over the past month, Corn's price has fallen 3.96%, and is down 4.36% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Corn - values, historical data, forecasts and news - updated on July of 2025.

Commodity Prices YoY in Australia decreased by 8.70 percent in June from -9.40 percent in May of 2025. This dataset includes a chart with historical data for Australia Commodity Prices YoY.

Wheat fell to 539.78 USd/Bu on July 24, 2025, down 0.13% from the previous day. Over the past month, Wheat's price has risen 2.18%, and is up 0.38% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Wheat - values, historical data, forecasts and news - updated on July of 2025.

This table contains 7 series, with data starting from 1972 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 item: Canada), Commodity (7 items: Total, all commodities; Total excluding energy; Energy; Metals and Minerals; ...).

This table contains 5 series, with data for years 1972 - 2010 (not all combinations necessarily have data for all years), and was last released on 2010-05-12. This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...) Commodity (5 items: Total; all commodities; Food; Total excluding energy; Energy ...).

The price index of natural gas dropped sharply in October 2022 after having reached around 893 points in August 2022 relative to the base year of 2016. By August 2024, coal had the highest consumer price index of the selected commodities at 196.6. In other words, coal prices worldwide were nearly two times higher in that month than in 2016. The cost of several commodities, especially energy resources, rose at the end of February 2022 after the Russian invasion of Ukraine.

This table contains 23 series, with data for years 1956 - 2013 (not all combinations necessarily have data for all years), and was last released on 2014-01-06. This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...), Commodities (23 items: Total; all commodities; Fruit; vegetable; feeds and other food products; Meat; fish and dairy products; Total; excluding petroleum and coal products ...).

Long term price performance of all major primary commodities. This includes the 3 months, 6 months, 1 year, 3 year, as well as the 5 year change in %.

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.