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.

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.

OECD Global Crude Oil Imports Prices

Regional and Temporal Trends

By OECD [source]

About this dataset

This dataset contains global crude oil import prices from the OECD. It provides important insight into international trading of oil and its related products, enabling users to analyse market trends and compare prices across different countries. This data is essential for understanding the development of different economies, as well as their dependence on crude oil imports. Through analysis of this dataset, users can understand the role that regional and global factors play in impacting global crude oil import prices over time. The dataset includes columns tracking country/region of origin (LOCATION), indicator measured (INDICATOR), subject tracked (SUBJECT), measure taken (MEASURE), frequency interval (FREQUENCY), time period covered (TIME) as well as numerical value and flag codes associated with the data captured in each row. This invaluable source is perfect for researchers looking to take a deep dive into international markets over time or academics studying the complexities surrounding trade in the energy sector!

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How to use the dataset

This dataset is a great resource for anyone looking to analyze the current and historical prices of crude oil imports from the OECD. The data contains prices from member countries of the OECD and is updated regularly. This dataset can be used to study long term trends in price as well as explore differences between countries with different levels of crude oil import demand.

• In order to make use of this dataset, it’s important to familiarize yourself with the column names and descriptions. The first column is LOCATION which indicates which country or region the data applies to. INDICATOR indicates what information is being displayed (e.g., import market share, import value, etc.). SUBJECT describes what category that metric falls into (e.g., fuel energy). MEASURE tells you whether an amount is expressed in a unit or currency while FREQUENCY says how often data has been collected: monthly, quarterly or annually (average monthly/quarterly/annual etc..). TIME displays measure period start date in year-month format and Value denotes numerical value for each row's measurement respectively while flag codes indicate if any values are estimates or outlier measurements that should be examined further before using them

• Using this understanding, one could filter their search by creating filters on these columns accordingly depending on their research topic such as – pulling all records for China for Q4 2019 - then apply sorting on “VALUE” column based on imported measurements have become cheaper during given time frame etc.. Additionally formulas like SUMIFS() can also be used across multiple columns available within this agreement document at same time such as – total Imports Value from India & Japan combined during May 2019 till October 2020 – based upon bringing together Matching condition criteria met across few columns where needed at same time . As such this dataset provides flexible solutions which potentially allow us to explore patterns related either just single country's current trends -or- cross references since global side-by-side evaluation possible here featuring more than just one nation alone too ...........

Research Ideas

• Analyzing the impact of changes in crude oil prices on global economic growth.
• Examining the evolving dynamics of crude oil trade flows between different countries and regions.
• Tracking trends in crude oil import prices across different industries to identify potential opportunities for cost savings and efficiency gains

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: crude_oil_import_prices.csv | Column name | Description ...

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.

Monthly and long-term wti crude oil price data (US$/bbl): historical series and analyst forecasts curated by FocusEconomics.

Heating Oil Consumption And Cost (2010 - 2022)

Consumption and cost of heating oil (2010 - 2022)

By data.world's Admin [source]

About this dataset

How much heating oil do New Yorkers use? And how much does it cost them? This dataset provides monthly consumption and cost data for heating oil in New York City, broken down by borough and development. The data includes information on utility vendors and meters, making it possible to track trends over time

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How to use the dataset

This dataset contains monthly consumption and cost data for heating oil in New York City. The data is organized by borough and development, and includes information on the utility vendor and meter used.

This dataset can be used to track trends in heating oil consumption and cost over time, as well as to compare consumption and costs across different developments in New York City

Research Ideas

Predicting future heating oil consumption trends in New York City Analyzing the impact of weather on heating oil consumption Determining the most efficient heating oil providers in New York City

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: heating-oil-consumption-and-cost-2010-feb-2022-1.csv | Column name | Description | |:-----------------------|:----------------------------------------------------------| | Development Name | The name of the development. (String) | | Borough | The borough in which the development is located. (String) | | Account Name | The name of the account. (String) | | Location | The location of the development. (String) | | Meter AMR | The meter's AMR reading. (String) | | Meter Scope | The meter's scope. (String) | | TDS # | The TDS number. (String) | | EDP | The EDP. (String) | | RC Code | The RC code. (String) | | Funding Source | The funding source. (String) | | AMP # | The AMP number. (String) | | Vendor Name | The name of the vendor. (String) | | Revenue Month | The revenue month. (String) | | Service Start Date | The service start date. (String) | | Service End Date | The service end date. (String) | | # days | The number of days in the service period. (String) | | Meter Number | The meter number. (String) | | Estimated | Whether or not the consumption is estimated. (String) | | Current Charges | The current charges. (String) | | Consumption (GAL) | The consumption in gallons. (String) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit data.world's Admin.

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...

The Average Home Heating Oil Prices dataset provides New York residents and businesses with objective information on average residential retail heating fuel oil pricing in New York State and by region beginning September 8, 1997. Pricing data is obtained via surveys conducted by NYSERDA staff on a weekly basis during heating season (September to March) and bi-weekly during the rest of the year. All prices are listed in dollars per gallon. The Average Home Heating Oil Prices dataset, Average Residential Retail Kerosene Prices dataset, and Average Residential Retail Propane Prices dataset are collectively referred to as the Heating Fuel Prices dataset. For current and historical residential retail price data, regional comparisons, and fuel type comparisons, please visit the Home Heating Oil Prices Dashboard: https://www.nyserda.ny.gov/Researchers-and-Policymakers/Energy-Prices/Home-Heating-Oil/Average-Home-Heating-Oil-Prices The New York State Energy Research and Development Authority (NYSERDA) offers objective information and analysis, innovative programs, technical expertise, and support to help New Yorkers increase energy efficiency, save money, use renewable energy, accelerate economic growth, and reduce reliance on fossil fuels. To learn more about NYSERDA’s programs, visit nyserda.ny.gov or follow us on X, Facebook, YouTube, or Instagram.

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.

Monthly average retail prices for gasoline and fuel oil for Canada, selected provincial cities, Whitehorse and Yellowknife. Prices are presented for the current month and previous four months. Includes fuel type and the price in cents per litre.

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.

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

Nigeria Crude Oil Price: Bonny Light: per Barrel data was reported at 65.350 USD/Barrel in 26 Nov 2025. This records an increase from the previous number of 65.050 USD/Barrel for 25 Nov 2025. Nigeria Crude Oil Price: Bonny Light: per Barrel data is updated daily, averaging 67.540 USD/Barrel from Oct 2009 (Median) to 26 Nov 2025, with 3752 observations. The data reached an all-time high of 139.410 USD/Barrel in 08 Mar 2022 and a record low of 7.150 USD/Barrel in 21 Apr 2020. Nigeria Crude Oil Price: Bonny Light: per Barrel data remains active status in CEIC and is reported by Central Bank of Nigeria. The data is categorized under Global Database’s Nigeria – Table NG.P: Crude Oil Price. [COVID-19-IMPACT]

Monthly and long-term brent crude oil price data (US$/bbl): historical series and analyst forecasts curated by FocusEconomics.

This dataset contains India Oil Database for 2002-2021. Data from Joint Organisations Data Initiative. Follow datasource.kapsarc.org for timely data to advance energy economics research.

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