Baumeister and Kilian (Journal of Business and Economic Statistics, 2015, 33(3), 338-351) combine forecasts from six empirical models to predict real oil prices. In this paper, we broadly reproduce their main economic findings, employing their preferred measures of the real oil price and other real-time variables. Mindful of the importance of Brent crude oil as a global price benchmark, we extend consideration to the North Sea-based measure and update the evaluation sample to 2017:12. We model the oil price futures curve using a factor-based Nelson-Siegel specification estimated in real time to fill in missing values for oil price futures in the raw data. We find that the combined forecasts for Brent are as effective as for other oil price measures. The extended sample using the oil price measures adopted by Baumeister and Kilian yields similar results to those reported in their paper. Also, the futures-based model improves forecast accuracy at longer horizons.

Sunflower Oil fell to 1,248.10 USD/T on June 6, 2025, down 0.41% from the previous day. Over the past month, Sunflower Oil's price has fallen 3.95%, but it is still 35.50% higher than a year ago, 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 Sunflower Oil.

Natural gas account for 1/4 of the global demand and roughly 1/3 of the US energy demand. After oil, Natural gas is the most dominate sort of energy. So, being about to improve natural gas demand prediction is extremely valuable.

Therefore, this project aims to predict the demand of Natural Gas in the US by combining a wide range of datasets including the time series of major Natural Gas Prices including US Henry Hub. Data comes from U.S. Energy Information Administration. Need to forecast the price of natural gas based on the historical data.

Data

Dataset contains Daily prices of Natural gas, starting from January 1997 to current year. Prices are in nominal dollars.

Brent crude oil is projected to have an average annual spot price of 65.85 U.S. dollars per barrel in 2025, according to a forecast from May 2025. This would mean a decrease of nearly 15 U.S. dollars compared to the previous year, and also reflects a reduced forecast WTI crude oil price. Lower economic activity, an increase in OPEC+ production output, and uncertainty over trade tariffs all impacted price forecasting. All about Brent Also known as Brent Blend, London Brent, and Brent petroleum, Brent Crude is a crude oil benchmark named after the exploration site in the North Sea's Brent oilfield. It is a sweet light crude oil but slightly heavier than West Texas Intermediate. In this context, sweet refers to a low sulfur content and light refers to a relatively low density when compared to other crude oil benchmarks. Price development in the 2020s Oil prices are volatile, impacted by consumer demand and discoveries of new oilfields, new extraction methods such as fracking, and production caps routinely placed by OPEC on its member states. The price for Brent crude oil stood at an average of just 42 U.S. dollars in 2020, when the coronavirus pandemic resulted in a sudden demand drop. Two years later, sanctions on Russian energy imports, had pushed up prices to a new decade-high, above 100 U.S. dollars per barrel.

Gasoline rose to 2.09 USD/Gal on June 9, 2025, up 0.36% from the previous day. Over the past month, Gasoline's price has fallen 2.01%, and is down 13.72% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Gasoline - values, historical data, forecasts and news - updated on June of 2025.

Deterministic and stochastic are two methods for modeling of crude oil and bottled water market. Forecasting the price of the market directly affected energy producer and water user.There are two software, Tableau and Python, which are utilized to model and visualize both markets for the aim of estimating possible price in the future.The role of those software is to provide an optimal alternative with different methods (deterministic versus stochastic). The base of predicted price in Tableau is deterministic—global optimization and time series. In contrast, Monte Carlo simulation as a stochastic method is modeled by Python software. The purpose of the project is, first, to predict the price of crude oil and bottled water with stochastic (Monte Carlo simulation) and deterministic (Tableau software),second, to compare the prices in a case study of Crude Oil Prices: West Texas Intermediate (WTI) and the U.S. bottled water. 1. Introduction Predicting stock and stock price index is challenging due to uncertainties involved. We can analyze with a different aspect; the investors perform before investing in a stock or the evaluation of stocks by means of studying statistics generated by market activity such as past prices and volumes. The data analysis attempt to identify stock patterns and trends that may predict the estimation price in the future. Initially, the classical regression (deterministic) methods were used to predict stock trends; furthermore, the uncertainty (stochastic) methods were used to forecast as same as deterministic. According to Deterministic versus stochastic volatility: implications for option pricing models (1997), Paul Brockman & Mustafa Chowdhury researched that the stock return volatility is deterministic or stochastic. They reported that “Results reported herein add support to the growing literature on preference-based stochastic volatility models and generally reject the notion of deterministic volatility” (Pag.499). For this argument, we need to research for modeling forecasting historical data with two software (Tableau and Python). In order to forecast analyze Tableau feature, the software automatically chooses the best of up to eight models which generates the highest quality forecast. According to the manual of Tableau , Tableau assesses forecast quality optimize the smoothing of each model. The optimization model is global. The main part of the model is a taxonomy of exponential smoothing that analyzes the best eight models with enough data. The real- world data generating process is a part of the forecast feature and to support deterministic method. Therefore, Tableau forecast feature is illustrated the best possible price in the future by deterministic (time – series and prices). Monte Carlo simulation (MCs) is modeled by Python, which is predicted the floating stock market index . Forecasting the stock market by Monte Carlo demonstrates in mathematics to solve various problems by generating suitable random numbers and observing that fraction of the numbers that obeys some property or properties. The method utilizes to obtain numerical solutions to problems too complicated to solve analytically. It randomly generates thousands of series representing potential outcomes for possible returns. Therefore, the variable price is the base of a random number between possible spot price between 2002-2016 that present a stochastic method.

UK Gas fell to 82.28 GBp/thm on June 9, 2025, down 3.15% from the previous day. Over the past month, UK Gas's price has fallen 1.45%, but it is still 0.95% higher than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. UK Natural Gas - values, historical data, forecasts and news - updated on June of 2025.

This data set is used in the Master's thesis: "A Comparison of Price Fluctuations Between Brent Crude Oil and Retail Fuel Prices in Stavanger - An Algorithmic Model for Refueling" by Ola Nes (2021) The data set contains the fuel prices collected (Excel and CSV files), and the Python code which contains all functions used in the thesis. Abstract for thesis: "This thesis investigates and compares the volatility in the retail fuel market in Stavanger and Brent crude oil. Gasoline and diesel prices have been collected from gas stations in Stavanger in 2020 and 2021, and are used for the thesis’ main goal of developing an algorithmic mathematical model for refueling vehicles at optimal times for consumers that could be used in practice. The collected data suggests that there is higher volatility in the retail fuel market in Stavanger compared to the Brent crude oil market. Gas stations follow a characteristic Edgeworth cycle pattern that have price spikes occur when restarting their price cycles. These occur for the most part at the same time across all gas stations monitored in Stavanger. This pattern can be difficult for consumers to predict. Therefore, a practical refueling algorithm could be useful. There are many factors that go in to such a model to make it efficient such as price spike analysis from the Edgeworth cycle pattern found in retail fuel markets and estimating volatility using GARCH(1,1) method."

Get the latest insights on price movement and trend analysis of Pyrolysis Oil in different regions across the world (Asia, Europe, North America, Latin America, and the Middle East & Africa).

Title: Natural Gas Price Determinants: A Comprehensive Dataset

Description: This dataset provides a rich and detailed exploration of various factors influencing natural gas prices, offering valuable insights for researchers, analysts, and policymakers. The data is sourced from AEMO (Australian Energy Market Operator), ensuring the highest level of accuracy and reliability.

Key Features: Comprehensive Parameter Coverage: The dataset includes a wide range of variables relevant to natural gas pricing, such as: Supply Factors: Gas production rates, storage levels, and pipeline capacities. * Demand Factors: Consumption patterns, industrial usage, and residential demand. * Economic Indicators: GDP growth, inflation rates, and consumer confidence. * Weather Conditions: Temperature variations, precipitation, and extreme weather events. * Geopolitical Factors: International conflicts, trade policies, and regulatory changes. * Time Series Data: The dataset spans multiple years, allowing for in-depth analysis of price trends, seasonality, and long-term correlations. * Granular Level of Detail: Data is provided at a granular level, enabling detailed examination of price fluctuations across different regions and time periods. * Clean and Standardized Format: The dataset is carefully curated and standardized to ensure data quality and consistency.

Potential Use Cases:

• Price Forecasting: Researchers can develop accurate models to predict future natural gas prices based on historical data and identified trends.
• Risk Assessment: Analysts can assess the impact of various factors on price volatility and identify potential risks for market participants.
• Policy Analysis: Policymakers can evaluate the effectiveness of different regulatory measures and pricing strategies.
• Investment Decisions: Investors can make informed decisions about investments in the natural gas sector by understanding the underlying drivers of price fluctuations.

Dataset Format:

• CSV: The dataset will be provided in a CSV format for easy import and analysis using popular data science tools.

Acknowledgements:

We would like to thank AEMO for providing the data and supporting this research.

Keywords: natural gas, price, determinants, factors, AEMO, dataset, analysis, forecasting, risk, policy, investment.

Saudi Arabia Fuel Prices: Retail: Gasoline 91 data was reported at 2.180 SAR/l in Apr 2025. This stayed constant from the previous number of 2.180 SAR/l for Mar 2025. Saudi Arabia Fuel Prices: Retail: Gasoline 91 data is updated monthly, averaging 2.180 SAR/l from Jul 2020 (Median) to Apr 2025, with 58 observations. The data reached an all-time high of 2.180 SAR/l in Apr 2025 and a record low of 1.290 SAR/l in Jul 2020. Saudi Arabia Fuel Prices: Retail: Gasoline 91 data remains active status in CEIC and is reported by Saudi Arabian Oil Company. The data is categorized under Global Database’s Saudi Arabia – Table SA.P016: Fuel Prices. [COVID-19-IMPACT]

Cocoa rose to 10,208.10 USD/T on June 9, 2025, up 0.34% from the previous day. Over the past month, Cocoa's price has risen 9.29%, and is up 7.62% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Cocoa - values, historical data, forecasts and news - updated on June of 2025.

Wheat fell to 544.29 USd/Bu on June 9, 2025, down 1.89% from the previous day. Over the past month, Wheat's price has risen 5.64%, but it is still 10.40% lower than a year ago, 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 June of 2025.

Get the latest insights on price movement and trend analysis of Gasoline in different regions across the world (Asia, Europe, North America, Latin America, and the Middle East & Africa).

Hascol

Gasoline Prices in Norway decreased to 1.90 USD/Liter in April from 1.97 USD/Liter in March of 2025. This dataset provides the latest reported value for - Norway Gasoline Prices - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Soybeans fell to 1,055.75 USd/Bu on June 9, 2025, down 0.14% from the previous day. Over the past month, Soybeans's price has fallen 1.45%, and is down 11.15% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Soybeans - values, historical data, forecasts and news - updated on June of 2025.

Gasoline Prices in Lebanon decreased to 0.78 USD/Liter in May from 0.79 USD/Liter in April of 2025. This dataset provides the latest reported value for - Lebanon Gasoline Prices - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Gasoline Prices in Egypt increased to 0.35 USD/Liter in May from 0.34 USD/Liter in April of 2025. This dataset provides - Egypt Gasoline Prices- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Corn fell to 442.02 USd/BU on June 9, 2025, down 0.11% from the previous day. Over the past month, Corn's price has fallen 1.34%, and is down 2.15% 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 June of 2025.