Natural gas rose to 4.94 USD/MMBtu on December 3, 2025, up 2.04% from the previous day. Over the past month, Natural gas's price has risen 13.71%, and is up 62.29% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Natural gas - values, historical data, forecasts and news - updated on December of 2025.

Learn about natural gas WTI, its role as a benchmark for natural gas and crude oil, and how trading futures contracts can help manage price risk and speculate on price movements.

Learn about WTI natural gas prices and how they are influenced by factors such as supply and demand, weather conditions, geopolitical events, and economic indicators. Discover why understanding these factors is crucial for traders and investors in the natural gas market.

TTF Gas fell to 27.92 EUR/MWh on December 3, 2025, down 0.17% from the previous day. Over the past month, TTF Gas's price has fallen 14.22%, and is down 40.94% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. EU Natural Gas - values, historical data, forecasts and news - updated on December of 2025.

Historical prices of Brent Oil, Crude Oil WTI, Natural Gas, Heating Oil from 2000-2022.

cover image credit: https://www.pexels.com/photo/blaze-blue-blur-bright-266896/

View market daily updates and historical trends for Crude Oil to Natural Gas Price Ratio. Source: Energy Information Administration. Track economic data w…

Commodity Prices: Corn, soybeans, WTI crude oil and Henry Hub natural gas, Chicago Mercantile Exchange Group .

United States' electricity producers paid about 2.75 U.S. dollars per million British thermal unit for natural gas in 2024. Meanwhile, coal power plant operators paid an average of 2.48 U.S. dollars. In the last decade, the price of natural gas used for electricity generation has seen a net decrease, followed by a considerable rise in 2022. Coal, on the other hand, has consistently been among the cheapest fuel types used in the power sector. Natural gas prices and the influence of oil demand As it is often produced alongside oil, prices for natural gas are shaped by overall market developments of the oil and gas industry. When an overproduction of oil led to the oil glut between 2015 and 2016, natural gas prices fell notably. The same circumstance could be observed in 2020 when a fall in oil demand brought many benchmarks such as WTI and Brent to historic lows and also resulted in the Henry Hub price falling to a 21-year low. Apart from petroleum, which is an expensive and inefficient means of power production, fossil fuel costs for electricity generation have declined since 2022. Shift away from conventional energy sources Although renewable technologies were once thought to be very expensive, greater investments have quickly rendered their levelized cost of energy generation on par with fossil fuels, especially when deployed on a utility-scale. The aging coal fleet is a prime example of the increasing necessity to switch to carbon neutral technologies. Older coal plants are dealing with increasing maintenance costs as well as environmental regulations forcing the installation of pollution controls.

The data is in Stata format and includes 2 files. The file named Agric has variables: spot price of Chicago corn and Chicago soybeans, the futures price of Chicago corn and Chicago soybeans and long positions of commodity index traders. The file named Energy contains variables on spot and futures prices of WTI crude oil and Henry Hub natural gas. The data is originally obtained from US commodity futures trading commission

This dataset contains monthly historical prices of 10 different commodities from January 1980 to April 2023. The data was collected from the Alpha Vantage API using Python. The commodities included in the dataset are WTI crude oil, cotton, natural gas, coffee, sugar, aluminum, Brent crude oil, corn, copper, and wheat. Prices are reported in USD per unit of measurement for each commodity. The dataset contains 520 rows and 12 columns, with each row representing a monthly observation of the prices of the 10 commodities. The 'All_Commodities' column is new.

WTI: WTI crude oil price per unit of measurement (USD). COTTON: Cotton price per unit of measurement (USD). NATURAL_GAS: Natural gas price per unit of measurement (USD). ALL_COMMODITIES: A composite index that represents the average price of all 10 commodities in the dataset, weighted by their individual market capitalizations. Prices are reported in USD per unit of measurement. COFFEE: Coffee price per unit of measurement (USD). SUGAR: Sugar price per unit of measurement (USD). ALUMINUM: Aluminum price per unit of measurement (USD). BRENT: Brent crude oil price per unit of measurement (USD). CORN: Corn price per unit of measurement (USD). COPPER: Copper price per unit of measurement (USD). WHEAT: Wheat price per unit of measurement (USD).

Note that some values are missing in the dataset, represented by NaN. These missing values occur for some of the commodities in the earlier years of the dataset.

It may be useful for time series analysis and predictive modeling.

NaN values were included so that you as a Data Scientist can get some practice on dealing with NaN values.

https://www.alphavantage.co/documentation/

Crude Oil fell to 59.17 USD/Bbl on December 2, 2025, down 0.25% from the previous day. Over the past month, Crude Oil's price has fallen 3.08%, and is down 15.40% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Crude Oil - values, historical data, forecasts and news - updated on December of 2025.

On October 27, 2025, the Brent crude oil price stood at 65.14 U.S. dollars per barrel, compared to 61.31 U.S. dollars for WTI oil and 67.54 U.S. dollars for the OPEC basket. Oil prices rose slightly that week.Europe's Brent crude oil, the U.S. WTI crude oil, and OPEC's basket are three of the most important benchmarks used by traders as reference for global oil and gasoline prices. Lowest ever oil prices during coronavirus pandemic In 2020, the coronavirus pandemic resulted in crude oil prices hitting a major slump as oil demand drastically declined following lockdowns and travel restrictions. Initial outlooks and uncertainty surrounding the course of the pandemic brought about a disagreement between two of the largest oil producers, Russia and Saudi Arabia, in early March. Bilateral talks between global oil producers ended in agreement on April 13th, with promises to cut petroleum output and hopes rising that these might help stabilize the oil price in the coming weeks. However, with storage facilities and oil tankers quickly filling up, fears grew over where to store excess oil, leading to benchmark prices seeing record negative prices between April 20 and April 22, 2020. How crude oil prices are determined As with most commodities, crude oil prices are impacted by supply and demand, as well as inventories and market sentiment. However, as oil is most often traded in future contracts (where a contract is agreed upon while product delivery will follow in the next two to three months), market speculation is one of the principal determinants for oil prices. Traders make conclusions on how production output and consumer demand will likely develop over the coming months, leaving room for uncertainty. Spot prices differ from futures in so far as they reflect the current market price of a commodity.

Dataset

This dataset was created by Spencer Tollefson

Contents

This dataset contains historical daily closing prices for significant natural resource futures contracts. It includes comprehensive data covering a range of key commodities such as Crude Oil (WTI and Brent), Gold, Silver, Natural Gas, Corn, Wheat, Soybean, Copper, Platinum, and Palladium.

The dataset provided is particularly suitable for a variety of analytical and predictive purposes, including:

Market trend analysis and visualization to understand price fluctuations and long-term cycles.

Economic research to assess the impact of economic events, policy changes, or supply-demand dynamics on commodity prices.

Building forecasting models, including machine learning and time series predictive analytics, to predict future price movements.

Portfolio optimization and risk management by analyzing commodity correlations and volatility.

Educational purposes, offering practical datasets for training students in economics, finance, statistics, and data science courses.

Due to the nature of historical data collection, certain entries may be missing. For example, there is an instance where an entire month of Platinum price data is absent. Such missing data points can typically be handled effectively by imputing values—calculating an average between the last known entry and the subsequent available entry—without negatively impacting the integrity of analytical studies.

The 2025 preliminary average annual price of West Texas Intermediate crude oil reached 67.83 U.S. dollars per barrel as of August. This would be nine U.S. dollars below the 2024 average and the lowest annual average since 2021. WTI and other benchmarks WTI is a grade of crude oil also known as “Texas light sweet.” It is measured to have an API gravity of around 39.6 and specific gravity of about 0.83, which is considered “light” relative to other crude oils. This oil also contains roughly 0.24 percent sulfur, and is therefore named “sweet.” Crude oils are some of the most closely observed commodity prices in the world. WTI is the underlying commodity of the Chicago Mercantile Exchange’s oil futures contracts. The price of other crude oils, such as UK Brent crude oil, the OPEC crude oil basket, and Dubai Fateh oil, can be compared to that of WTI crude oil. Since 1976, the price of WTI crude oil has increased notably, rising from just 12.23 U.S. dollars per barrel in 1976 to a peak of 99.06 dollars per barrel in 2008. Geopolitical conflicts and their impact on oil prices The price of oil is controlled in part by limiting oil production. Prior to 1971, the Texas Railroad Commission controlled the price of oil by setting limits on production of U.S. oil. In 1971, the Texas Railroad Commission ceased limiting production, but OPEC, the Organization of Petroleum Exporting Countries with member states Iran, Iraq, Kuwait, Saudi Arabia, and Venezuela among others, continued to do so. In 1972, due to geopolitical conflict, OPEC set an oil embargo and cut oil production, causing prices to quadruple by 1974. Oil prices rose again in 1979 and 1980 due to the Iranian revolution, and doubled between 1978 and 1981 as the Iran-Iraq War prevented oil production. A number of geopolitical conflicts and periods of increased production and consumption have influenced the price of oil since then.

The datasets for the Role of Financial Investors on Commodity Futures Risk Premium are weekly datasets for the period from 1995 to 2015 for three commodities in the energy market: crude oil (WTI), heating oil, and natural gas. These datasets contain futures prices for different maturities, open interest positions for each commodity (long and short open interest positions), and S&P 500 composite index. The selected commodities are traded on the New York Mercantile Exchange (NYMEX). The data comes from the Thomson Reuters Datastream and from the Commodity Futures Trading Commission (CFTC).

The 2025 annual OPEC basket price stood at ***** U.S. dollars per barrel as of August. This would be lower than the 2024 average, which amounted to ***** U.S. dollars. The abbreviation OPEC stands for Organization of the Petroleum Exporting Countries and includes Algeria, Angola, Congo, Equatorial Guinea, Gabon, Iraq, Iran, Kuwait, Libya, Nigeria, Saudi Arabia, Venezuela, and the United Arab Emirates. The aim of the OPEC is to coordinate the oil policies of its member states. It was founded in 1960 in Baghdad, Iraq. The OPEC Reference Basket The OPEC crude oil price is defined by the price of the so-called OPEC (Reference) basket. This basket is an average of prices of the various petroleum blends that are produced by the OPEC members. Some of these oil blends are, for example: Saharan Blend from Algeria, Basra Light from Iraq, Arab Light from Saudi Arabia, BCF 17 from Venezuela, et cetera. By increasing and decreasing its oil production, OPEC tries to keep the price between a given maxima and minima. Benchmark crude oil The OPEC basket is one of the most important benchmarks for crude oil prices worldwide. Other significant benchmarks are UK Brent, West Texas Intermediate (WTI), and Dubai Crude (Fateh). Because there are many types and grades of oil, such benchmarks are indispensable for referencing them on the global oil market. The 2025 fall in prices was the result of weakened demand outlooks exacerbated by extensive U.S. trade tariffs.

You are members of the analytic department in one of the Alberta Oil Sands extraction companies. You are given a current project to collect and clean the data and choose, fit and validate the model for further continuous prediction of demand for the company's products. This will allow the company to assess profitability and to set the appropriate volumes of production.

In short, you need to use the historical data of https://www.eia.gov/dnav/pet/hist/rwtcW.htm in its weekly version, and to predict it for the available weeks of 2021, to evaluate the quality of your prediction and to compose a report for your management. Before working with real data, you first check the intended model on simulated data.

Below we suggest the specific steps of analysis for those who like the detailed instructions. However, these steps may be changed by those who prefer free creativity.

The moral that we are trying to learn in this assignment is that it is easy to forecast series generated by a certain family of models. However, it is hard to forecast the real cases.

We will study the necessary material for the whole semester. However, steps in italic, you can start immediately. I suggest you to start early, because the volume is high.

Part 1. Artificial data

1.1. Simulation.

1.1.1. For ARIMA(p, d, q), set p = 2
d = 1
q = 2
phi_1 = -.2
phi_2 = .15
theta_1 = .3
theta_2 = -.1
sd = 0.03
and generate a series of sample size n = 1000, using this model. 1.1.2. Add a linear trend y(t) = b0 + b1*t, using the coefficients intercept b0 = -1 and slope b1= 0.0015. 1.1.3. Apply an exponential function.

1.2. Exploratory analysis in the order, inverse to the simulation steps.

1.2.1. Divide the generated set into a training set head and a test set tail. 1.2.2. Logarithm the training set series. 1.2.3. Detect a linear trend by regression. Compare the estimated trend parameters to true ones. 1.2.4. Detrend the series. 1.2.5. In the same axes, plot the original ARIMA simulation and the current (trended, exponentiated, logarithmed and finally detrended) series. They should have the same shape, but differ by a bit of shift and stretch. 1.2.6. ARIMA fit. 1.2.6.1. By 3 nested loops over p, d and q between 0 and 3, print all values of AIC in 3 4-by-4-tables. Choose the triple, minimizing AIC. Compare it to the true (p, d, q) triple and comment. 1.2.6.2. Fit the model by auto.arima command. Comment on its choice of p, d and q, comparing to true values and those chosen by triple loop. 1.2.6.3. Leave out those attempts of order estimations and choose the true (p, d, q) triple. Fit ARIMA(2, 1, 2), using the function forecast::Arima, to the training data. 1.2.7. Compare the estimated ARIMA parameters to true ones. Comment on goodness of fit.

1.3. Forecasting the simulated series.

1.3.1. Forecast the testing part of the ARIMA, using forecast::forecast function. 1.3.2. Add the estimated trend. 1.3.3. Exponentiate that trended forecast.

1.4. Evaluation of the results

1.4.1. Plot the forecast values, prediction interval, and the real testing set in the same axes. 1.4.2. Plot acf of the testing set and its prediction, and ccf between them. 1.4.3. Plot the residuals and their acf. 1.4.4. Estimate the forecast error.

Part 2. Real data.

2.1. Data sourcing and visualization.

2.1.1. Read the dataset https://www.kaggle.com/statistics101guy/wti-spot-price-fob-dollars-per-barrel 2.1.2. Plot the series and its acf.

2.2. Exploratory analysis

2.2.1. Divide the series into a training set (up to 2020 inclusively) and testing set (2021). 2.2.2. Logarithm the series. 2.2.3. Estimate the linear trend by the least squares procedure. 2.2.4. Detrend the series. 2.2.5. By “auto.arima” command of “forecast” library, fit ARIMA(p, d, q) to the training data.

2.3. Forecasting in the order, inverse to the exploratory analysis steps

2.3.1. Using the “forecast” function of the “forecast” library, forecast your ARIMA model for the period of testing set. 2.3.2. Extrapolate your linear trend to this period and add it to your ARIMA forecast. 2.3.3. Exponentiate the result.

2.4. Evaluation of the forecast quality

2.4.1. Plot the forecast values, prediction interval, and the real testing set in the same axes. 2.4.2. Plot acf of the testing set and its prediction, and ccf between them. 2.4.3. Plot the residuals and their acf. 2.4.4. Estimate the forecast error. 2.4.5. Comment on the results

Part 3. Reporting

3.1. Compose a business report of about 10 pages length, following the approximate structure:

3.1.1. Title page, listing the group members, project title, school, course, submission date. 3.1.2. Executive summary, containing your view of the problem setting, brief description of the intended analysis and all that usually pertains to this section 3.1.3. Ana...

WTI oil stock price refers to the stock price of West Texas Intermediate (WTI) crude oil. Learn about the factors that impact the price of WTI oil, including supply and demand dynamics, geopolitical events, economic conditions, and market speculation. Understand the volatility and fluctuations in the WTI oil stock price, and how it can affect the broader energy sector and the stock prices of oil and gas companies.

{"The file dataset.xlsx contains structured financial time series data prepared for an event study analysis (ESA) examining market reactions to the onset of the Russia–Ukraine war. The dataset encompasses observations from a diversified selection of asset classes, including: (i) safe-haven assets (gold, silver, platinum, Bitcoin), (ii) energy commodities (Brent crude oil, WTI crude oil, natural gas, coal, EU Emissions Trading System (ETS) allowances), (iii) energy equities (68 stocks from the S&P 1500 classified under the Energy sector), (iv) the S&P 500 index, and (v) energy-related cryptocurrencies (Electrify.Asia, Energy Web Token, Grid+, Power Ledger, SunContract, Efforce, and WePower). For abnormal return estimation, each asset group is matched with an appropriate benchmark index in accordance with ESA methodology: the Dow Jones Precious Metals Index (for gold, silver, and platinum), the CMC Crypto 200 Index (for Bitcoin and energy-related cryptocurrencies), the S&P GSCI Index (for energy commodities), the S&P 500 Index (for energy equities), and the MSCI All Country World Index (ACWI) for the general market. Event date is set to February 24, 2022, marking the outbreak of the full-scale military invasion of Ukraine by the Russian Federation. The estimation window comprises 252 daily price observations ending on December 31, 2021 (inclusive). The event window extends from t = –30 to t = +30 relative to the event date. The dataset is formatted to facilitate empirical testing of market efficiency, price sensitivity to geopolitical shocks, and asset class-specific abnormal return dynamics."}