Dataset

This dataset was created by Chang Pham

Released under MIT

Contents

Daily and historical dataset showing WTI crude oil priced in grams of gold, including WTI price, gold price, and the oil-in-gold ratio. Charts, analysis, and downloadable CSV included.

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.

Spain Crude Oil Price: Brent North Sea data was reported at 81.870 USD/Barrel in Oct 2018. This records an increase from the previous number of 79.080 USD/Barrel for Sep 2018. Spain Crude Oil Price: Brent North Sea data is updated monthly, averaging 28.330 USD/Barrel from Jan 1986 (Median) to Oct 2018, with 394 observations. The data reached an all-time high of 133.030 USD/Barrel in Jul 2008 and a record low of 9.740 USD/Barrel in Jul 1986. Spain Crude Oil Price: Brent North Sea data remains active status in CEIC and is reported by Bank of Spain. The data is categorized under Global Database’s Spain – Table ES.P006: Crude Oil and Gold Price.

Daily oil price in Gold dataset

Gold fell to 4,199.97 USD/t.oz on December 2, 2025, down 0.75% from the previous day. Over the past month, Gold's price has risen 4.93%, and is up 58.92% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Gold - values, historical data, forecasts and news - updated on December of 2025.

Historical commodity (daily) price from 2000-2022 (March). 1. Gold 2. Palladium 3. Nickel 4. Brent Oil 5. Natural Gas 6. Wheat

• cover image credit: https://www.pexels.com/photo/industrial-machine-during-golden-hour-162568/

This dataset contains daily historical data of major financial instruments and indexes from January 1, 2015, to August 15, 2025 . It includes the following columns:

SPX – S&P 500 Index daily closing prices.

GLD – SPDR Gold Shares ETF daily adjusted closing prices.

USO – United States Oil Fund ETF daily adjusted closing prices.

SLV – iShares Silver Trust ETF daily adjusted closing prices.

EUR/USD – Daily Euro to US Dollar exchange rate.

The data was collected from Yahoo Finance using the yfinance Python library. The dataset is intended for research, analysis, and educational purposes.

Spain Gold Price: EUR data was reported at 34.010 EUR/g in Oct 2018. This records an increase from the previous number of 33.060 EUR/g for Sep 2018. Spain Gold Price: EUR data is updated monthly, averaging 20.085 EUR/g from Jan 1999 (Median) to Oct 2018, with 238 observations. The data reached an all-time high of 43.590 EUR/g in Sep 2012 and a record low of 7.780 EUR/g in Aug 1999. Spain Gold Price: EUR data remains active status in CEIC and is reported by Bank of Spain. The data is categorized under Global Database’s Spain – Table ES.P006: Crude Oil and Gold Price.

1. Size: 3,904 rows × 47 columns
2. Content: The dataset contains financial market data related to gold price prediction. It includes:
3. Stock market indices (S&P 500, Nasdaq)
4. Precious metal prices (Gold, Silver, Platinum, Palladium)
5. Economic indicators (Interest rates, GDP, CPI)
6. Foreign exchange rates (USD/CHF, EUR/USD)

Oil price data Usage: Suitable for gold price regression analysis, financial forecasting, and market trend analysis.

EGPB

About Dataset This benchmark dataset consisting of 8030 rows and 36 variables sourced from multiple credible economic websites, covering a period from January 2001 to December 2022. This dataset can be utilized to predict gold prices specifically or to aid any economic field that is influenced by the variables in this dataset. Key variables & Features include: • Previous gold prices • Future gold prices with predictions for one day, one week, and one month • Oil prices •… See the full description on the dataset page: https://huggingface.co/datasets/Farah42/EGPBD-An-Event-based-Gold-Prices-Benchmark-Dataset.

Sunflower Oil rose to 1,426.40 INR/10 kg on December 2, 2025, up 0.68% from the previous day. Over the past month, Sunflower Oil's price has risen 0.79%, and is up 8.44% 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 Sunflower Oil.

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 reserved

Data

Dataset 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).

License

The 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.

Prices of Bitcoin, US stock indices, Gold and crude oil

This model will help us in knowing that how Crude oil price, interest rate (repo rate), Indian currency price in dollars, Sensex (BSE), Inflation rate and US Dollar index will follow a relationship with the gold price directly or indirectly.

The regression analysis in which we use one dependent variable and multiple independent variables is called a multivariate regression analysis. The forecasting plays an important role in econometrics and also helps to determine government policies with optimality. The business decision which are dependent on the prices of such commodities can make benefits from a feasible prediction. We will have a brief view over the error mean square values of the regression model which will guide us about the predictive ability of the predictive model . The data is wide spread across the time and is available from dated 1st October 2000 to 1 August 2020.

A prediction model is developed for the gold price in India dependent on 5 variables using the statistical interpretations from these variables. The independent variables taken were crude oil prices, USD to INR, Sensex, CPI and Interest rate. The model passes different aspects such as adjusted R squared, T test and Durbin Watson with high favoring values.

The model is passed as a perfect fit along with the residual analysis which depicts that the model is a good fit and acceptable. The data was taken for a long span of time period and there were no missing values which was favorable for the regression model. We could observe a strong relation between the gold price and USD to INR, CPI and Sensex values. In future, more variables can be a part of this model and the data can be for a longer time span leading to the other heights of optimality.

Forecast for the gold prices is created for the next 10 months ahead using ARIMA Model.

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 dataset was collected for the period spanning between 01/07/2019 and 31/12/2022.The historical Twitter volume were retrieved using ‘‘Bitcoin’’ (case insensitive) as the keyword from bitinfocharts.com. Google search volume was retrieved using library Gtrends. 2000 tweets per day using 4 times interval were crawled by employing Twitter API with the keyword “Bitcoin. The daily closing prices of Bitcoin, oil price, gold price, and U.S stock market indexes (S&P 500, NASDAQ, and Dow Jones Industrial Average) were collected using R libraries either Quantmod or Quandl.

Gold is one of the critical commodities that is used as a barometer of economic activity prevailing in the globalized world. The price of the gold is dependent on many economic indicators and is complex to understand the dynamics of its price discovery. To predict the prices of gold is paramount for any business involved in global trade and in turn gives indications of the overall financial stability of the global business environment. In this project, a system which is based on machine learning algorithms to predict the gold prices based on the historical data related to other closely related commodities and stock market indicators. The system is based on Random Forest Regression algorithm which is used to train on historical commodity prices such as Crude oil, Silver Price, Stock Price which are key indicators of the global financial markets and forms the core decision logic for future predictions. The results prove that the Random Forest Regressor Machine learning Algorithm performs better than the other methods with a forecasting accuracy of 98%.

Data set column

1. Date - mm/dd/yyyy
2. SPX - is a free-float weighted measurement stock market index of the 500 largest companies listed on stock exchanges in the United States.
3. GLD - Gold Price
4. USO - United States Oil Fund
5. SLV - Silver Price
6. EUR/USD - currency pair quotation of the Euro against the US

Forecasting gold prices remains vital in financial markets, given gold’s dual role as both a hedge against inflation and a safe-haven asset during economic uncertainty. This study proposes a hybrid model integrating SARIMA, LSTM, and RF to improve predictive accuracy by capturing both linear and nonlinear dependencies in historical gold price data. SARIMA models linear trends and seasonal components, LSTM captures nonlinear patterns from SARIMA residuals, and RF refines predictions using macroeconomic indicators such as the USD Index, Federal Interest Rate, US CPI, Oil Prices, S&P 500 Index, and Bond Yields. Utilizing real-world data, the model effectively tracks market trends with reduced forecasting errors, indicating continued price fluctuations and potential long-term growth. The findings provide valuable insights for investors and policymakers, with future research focusing on additional macroeconomic factors and advanced hybrid forecasting techniques. This study introduces a hybrid SARIMA–LSTM–RF model that enhances the accuracy of gold price forecasting by capturing both linear and nonlinear market dynamics. By integrating macroeconomic indicators such as the USD Index, Federal Interest Rate, CPI, Oil Prices, S&P 500 Index, and Bond Yields, the model effectively reflects real-world financial interactions influencing gold prices. The results demonstrate reduced prediction errors and improved tracking of short-term fluctuations as well as long-term growth trends. These findings provide valuable implications for investors and policymakers in managing financial risk and optimizing investment portfolios, while contributing to the advancement of hybrid forecasting frameworks for complex financial time series.