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.

Brent fell to 63.05 USD/Bbl on December 2, 2025, down 0.19% from the previous day. Over the past month, Brent's price has fallen 2.84%, and is down 14.36% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Brent crude oil - values, historical data, forecasts and news - updated on December of 2025.

The WTI (West Texas Intermediate) crude dataset is a time series dataset that contains daily historical prices of WTI crude oil. It provides the market price of crude oil per barrel in USD for each day from the past till the present.

This dataset contains information about world's crude oil prices for 1861-2020. Data from BP. Follow datasource.kapsarc.org for timely data to advance energy economics research.Notes: 1861-1944 US Average 1945-1983 Arabian Light posted at Ras Tanura 1984-2016 Brent dated. $2020 (deflated using the Consumer Price Index for the US

Heating Oil rose to 2.35 USD/Gal on December 2, 2025, up 0.21% from the previous day. Over the past month, Heating Oil's price has fallen 2.25%, but it is still 6.31% higher than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Heating oil - values, historical data, forecasts and news - updated on December of 2025.

Context

The crude oil price movements are subject to diverse influencing factors. This dataset was retrieved from the U.S. Energy Information Administration: Europe Brent Spot Price FOB (Dollars per Barrel)

Content

The aim of this dataset and work is to predict future Crude Oil Prices based on the historical data available in the dataset. The data contains daily Brent oil prices from 17th of May 1987 until the 13th of November 2022.

Acknowledgements

Dataset is available on U.S. Energy Information Administration: Europe Brent Spot Price FOB (Dollars per Barrel) which is updated on weekly bases.

Inspiration

The vast competition in the Data Science field and the availability of the new Prophet method made it easier to predict future prices, that is what you may find when predicting the oil prices with this dataset.

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.

Urals Oil fell to 54.22 USD/Bbl on December 1, 2025, down 0.37% from the previous day. Over the past month, Urals Oil's price has fallen 7.52%, and is down 17.95% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. This dataset includes a chart with historical data for Urals Crude.

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.

Oil prices trend

The data contains oil prices from 1998 from 8 countries.

Content

The data is self explanatory. The price is described in USD for the crude oil data and in local currencies for the fuel products.

Acknowledgements

The data is available from IEA website

Context

Data series employed in this study include 2197 daily observations of crude oil price, sustainability benchmark indices of the United States, and India for the period 2011-2019 daily. The crude oil prices (Brent crude in US dollars per barrel) are collected from the website of the U.S. Energy Information Administration (www.eia.gov). The Indian sustainability benchmark index is S&P BSE-Carbonex, and the Dow Jones Sustainability Index (DJSI), the US benchmark index for sustainability. The closing values of India and the US's sustainability indices are collected from the Bombay Stock Exchange (BSE) and S&P Global websites. The S&P BSE Carbonex index is intended to provide equity investors an opportunity to mitigate the climate change-related risks connected to their investments in the long run. It is based on S&P BSE 100 weights compared to the companies' carbon performance (level of greenhouse gas emissions). DJSI-US, the United States' sustainability index, has 126 constituents and ensures the investment option in U.S securities that applies sustainability practices (BSE, 2014; S&P Global, 2021).

Acknowledgements

A, BHUVANESKUMAR (2021), “Dataset on crude oil prices and sustainability indices of the USA and India”, Mendeley Data, V1, doi: 10.17632/c29vkmx2y2.1

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...

Context

Crude oil is the world's most critical energy source and one of the most actively traded commodities. Its price is a fundamental driver of the global economy, influencing everything from transportation costs and industrial production to inflation rates and geopolitical policy. This dataset tracks the price of West Texas Intermediate (WTI) crude oil, a high-quality grade that serves as a primary global benchmark.

Access to reliable, long-term historical data is crucial for economists, traders, and data scientists seeking to model market dynamics, analyze the impact of world events, and forecast economic trends. This dataset provides a comprehensive and daily-updated record of crude oil prices, sourced from the Crude Oil Futures (CL=F) market.

Content

This dataset contains daily price information for Crude Oil Futures (CL=F) in a clean, tabular format. Each row represents a single trading day and includes the following columns:

Date: The date of the trading session (YYYY-MM-DD).

Open: The price at which crude oil first traded for the day in USD per barrel.

High: The highest price reached during the trading day in USD per barrel.

Low: The lowest price reached during the trading day in USD per barrel.

Close: The closing price at the end of the trading day in USD per barrel.

Volume: The total number of futures contracts traded during the day.

The dataset includes monthly WTI crude oil spot and futures prices with the shortest maturity contracts (one-month, two-month, and three-month futures contracts), the US Ending Stocks of Crude Oil and Petroleum Products in thousands of barrels. All the datasets were sourced from US EIA, except for the three-month US treasury bill dataset sourced from the Federal Reserve Economic Data of St. Louis Federal Bank.

Russia Average World Price: Crude Oil: Urals: per 1 Barrel data was reported at 91.200 USD/Barrel in Mar 2019. This records an increase from the previous number of 80.700 USD/Barrel for Feb 2019. Russia Average World Price: Crude Oil: Urals: per 1 Barrel data is updated monthly, averaging 58.745 USD/Barrel from Jun 2000 (Median) to Mar 2019, with 226 observations. The data reached an all-time high of 129.710 USD/Barrel in Jul 2008 and a record low of 18.200 USD/Barrel in Nov 2001. Russia Average World Price: Crude Oil: Urals: per 1 Barrel data remains active status in CEIC and is reported by Ministry of Finance of the Russian Federation. The data is categorized under Global Database’s Russian Federation – Table RU.PC002: Average World Prices, Crude Oil Export Price, Crude Oil Export Duty.

As of August 2025, the average annual price of Brent crude oil stood at 71.3 U.S. dollars per barrel. This is over nine U.S. dollars lower than the 2024 average. Brent is the world's leading price benchmark for Atlantic basin crude oils. Crude oil is one of the most closely observed commodity prices as it influences costs across all stages of the production process and consequently alters the price of consumer goods as well. What determines crude oil benchmarks? In the past decade, crude oil prices have been especially volatile. Their inherent inelasticity regarding short-term changes in demand and supply means that oil prices are erratic by nature. However, since the 2009 financial crisis, many commercial developments have greatly contributed to price volatility, such as economic growth by BRIC countries like China and India, and the advent of hydraulic fracturing and horizontal drilling in the U.S. The outbreak of the coronavirus pandemic and the Russia-Ukraine war are examples of geopolitical events dictating prices. Light crude oils - Brent and WTI Brent Crude is considered a classification of sweet light crude oil and acts as a benchmark price for oil around the world. It is considered a sweet light crude oil due to its low sulfur content and low density and may be easily refined into gasoline. This oil originates in the North Sea and comprises several different oil blends, including Brent Blend and Ekofisk crude. Often, this crude oil is refined in Northwest Europe. Another sweet light oil often referenced alongside UK Brent is West Texas Intermediate (WTI). WTI oil prices amounted to 76.55 U.S. dollars per barrel in 2024.

Graph and download economic data for Crude Oil Prices: Brent - Europe (DCOILBRENTEU) from 1987-05-20 to 2025-11-03 about crude, oil, Europe, commodities, and price.

Palm Oil rose to 4,134 MYR/T on December 2, 2025, up 1.00% from the previous day. Over the past month, Palm Oil's price has risen 0.46%, but it is still 18.56% lower than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Palm Oil - values, historical data, forecasts and news - updated on December of 2025.

Crude_oil_value.csv Description:

The crude_oil_value.csv file contains historical data on the monetary value of crude oil trades over specific periods. This dataset is essential for analyzing market trends, economic impacts, and forecasting future trade values. Each entry represents a recorded trade, detailing the value in terms of currency, along with relevant timestamps and geographic locations.

Columns: Year: The year of the trade. Country: Malaysia. Trade_Value: The monetary value of the trade in USD. Trade_Type: The type of trade (import & export). Currency: The currency used for the trade value.

Crude_oil_volume.csv Description:

The crude_oil_volume.csv file contains data on the volume of crude oil traded over time. This dataset is crucial for understanding the physical quantities of oil being moved in the market, assessing production capabilities, and analyzing supply chain dynamics. Each entry includes details on the volume of crude oil traded, alongside temporal and spatial data.

Columns: Year: The year of the trade. Country: Malaysia. Trade_Volume: The volume of crude oil traded, typically measured in barrels. Trade_Type: The type of trade ( import & export). Volume_Unit: The unit of measurement for the volume (e.g., barrels).

Monthly and long-term Venezuela data: historical series and analyst forecasts curated by FocusEconomics.