Gold Prices - Historical chart and current data through 2025.

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.

Dataset of historical annual gold prices from 1970 to 2024, including significant events and acts that impacted gold prices.

This statistic depicts the average annual prices for gold from 2014 to 2024 with a forecast until 2026. In 2024, the average price for gold stood at 2,388 U.S. dollars per troy ounce, the highest value recorded throughout the period considered. In 2026, the average gold price is expected to increase, reaching 3,200 U.S. dollars per troy ounce.

The price of gold per troy ounce increased considerably between 1990 and 2025, despite some fluctuations. A troy ounce is the international common unit of weight used for precious metals and is approximately **** grams. At the end of 2024, a troy ounce of gold cost ******* U.S. dollars. As of * June 2025, it increased considerably to ******** U.S. dollars. Price of – additional information In 2000, the price of gold was at its lowest since 1990, with a troy ounce of gold costing ***** U.S. dollars in that year. Since then, gold prices have been rising and after the economic crisis of 2008, the price of gold rose at higher rates than ever before as the market began to see gold as an increasingly good investment. History has shown, gold is seen as a good investment in times of uncertainty because it can or is thought to function as a good store of value against a declining currency as well as providing protection against inflation. However, unlike other commodities, once gold is mined it does not get used up like other commodities (for example, such as gasoline). So while gold may be a good investment at times, the supply demand argument does not apply to gold. Nonetheless, the demand for gold has been mostly consistent.

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Gold Price Prediction in INR (2004-2024)🇮🇳 - Can You Forecast the Future? ✨

Data Description:

1. Time Period: October 2004 - December 2024 (20 years) - Long-term Trend Analysis!
2. Frequency: Monthly Average Gold Price (INR) - Uncover Seasonality!
3. Source: Financial Service Website (verified) ✅ - Reliable Data for Accurate Predictions!

Potential Applications:

• Time Series Analysis: Analyze trends and seasonality in gold prices. - Unravel the Gold Market Mystery! ️‍♀️
• Gold Price Prediction: Forecast future gold prices using ARIMA, SARIMA, fbProphet, etc. - Predict the Next Big Move!

Key Points:

• Long-Term Trend: Explore the evolution of gold prices over two decades. - See How Gold Has Changed Over Time! ⏳
• Prediction Models: Evaluate the effectiveness of different time series forecasting models. - Find the Best Tool for the Job!
• INR Focus: Gain insights specific to the Indian gold market. 🇮🇳 - Understand the Indian Gold Market Landscape! ️

⚠️ Note:

• Past performance does not guarantee future results.

Bonus:

This dataset allows you to explore the fascinating world of gold price prediction in the Indian market. Challenge yourself! Can you develop a model that outperforms the rest?

Silver fell to 57.28 USD/t.oz on December 2, 2025, down 1.22% from the previous day. Over the past month, Silver's price has risen 19.11%, and is up 84.81% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Silver - values, historical data, forecasts and news - updated on December of 2025.

Index Time Series for DB Gold Double Long ETN. The frequency of the observation is daily. Moving average series are also typically included. The index is intended to reflect changes in the market value of certain gold futures contracts and is comprised of a single unfunded gold futures contract.

Between January 1971 and May 2025, gold had average annual returns of **** percent, which was only slightly more than the return of commodities, with an annual average of around eight percent. The annual return of gold was over ** percent in 2024. What is the total global demand for gold? The global demand for gold remains robust owing to its historical importance, financial stability, and cultural appeal. During economic uncertainty, investors look for a safe haven, while emerging markets fuel jewelry demand. A distinct contrast transpired during COVID-19, when the global demand for gold experienced a sharp decline in 2020 owing to a reduction in consumer spending. However, the subsequent years saw an increase in demand for the precious metal. How much gold is produced worldwide? The production of gold depends mainly on geological formations, market demand, and the cost of production. These factors have a significant impact on the discovery, extraction, and economic viability of gold mining operations worldwide. In 2024, the worldwide production of gold was expected to reach *** million ounces, and it is anticipated that the rate of growth will increase as exploration technologies improve, gold prices rise, and mining practices improve.

Official statistics, Data of scientific publications.

Context

Real time prices in the diamond market are reflected by the so-called Diamond Financial Index (DFX) which is available on a daily base since April 2018.

As diamond prices are influenced by many factors like trade barriers, political instability, operational disruptions like mine closures or economic downturns resp. upturns, it is not an easy task to predict the development of future diamond prices.

To predict prices, indicators are needed. Empirical findings support the argument that diamond prices respond to economic downturns resp. upturns and are therefore also correlated with inflation rates and interest rates resp. fed rates. Also gold prices could be an indicator for the development of diamond prices.

Because the US are playing quite a big role in the diamond business, the following US rates can be considered:

o inflation rate (10-year breakeven inflation rate) o interest rate (10-year treasury inflation-indexed security, constant maturity, risk-free) o fed rate (effective federal funds rate)

Moreover, gold prices could be considered as an indicator.

Content

The following five datasets have been downloaded from the following websites and merged to one dataset:

o diamond price (DFX): https://www.investing.com/indices/get-diamonds-general o inflation rate: https://fred.stlouisfed.org/series/T10YIE o interest rate: https://fred.stlouisfed.org/series/DFII10 o fed rate: https://fred.stlouisfed.org/series/DFF o gold price: https://www.boerse-online.de/rohstoffe/historisch/goldpreis/usd/

To merge the datasets, date has been used as index. A few missing values in the datasets have been filled in by copying the value from the day before (see file "diamond_data_merged_with_other_variables.csv").

Please note: I added one additional version of the dataset where ID is used as index (not date). Missing values are not filled in in this version (see file "df_diamond_data_merged_with_other_variables.csv"). I would recommend using the dataset "diamond_data_merged_with_other_variables.csv" with date as index.

Inspiration

The following questions could be answered:

o How did diamond prices, inflation rate, interest rate, fed rate and gold price develop since 2018? o How is the correlation between diamond prices and inflation rate, interest rate, fed rate and gold prices? o How will diamond prices develop in the future?

When it comes to price prediction machine learning has been successful in predicting stock market prices through a host of different time series models. There is also a limited but quite restrictive application in predicting cryptocurrency prices. Often neural networks like LSTM (Long Short Term Memory) are used. LSTM oder other models, e.g. ARIMA, could be also used here.

Banking and stock markets consider gold to be an important component of their economic and financial status. There are various factors that influence the gold price trend and its fluctuations. Accurate and reliable prediction of the gold price is an essential part of financial and portfolio management. Moreover, it could provide insights about potential buy and sell points in order to prevent financial damages and reduce the risk of investment. In this paper, different architectures of deep neural network (DNN) have been proposed based on long short-term memory (LSTM) and convolutional-based neural networks (CNN) as a hybrid model, along with automatic parameter tuning to increase the accuracy, coefficient of determination, of the forecasting results. An illustrative dataset from the closing gold prices for 44 years, from 1978 to 2021, is provided to demonstrate the effectiveness and feasibility of this method. The grid search technique finds the optimal set of DNNs’ parameters. Furthermore, to assess the efficiency of DNN models, three statistical indices of RMSE, RMAE, and coefficient of determination (R2), were calculated for the test set. Results indicate that the proposed hybrid model (CNN-Bi-LSTM) outperforms other models in total bias, capturing extreme values and obtaining promising results. In this model, CNN is used to extract features of input dataset. Furthermore, Bi-LSTM uses CNN’s outputs to predict the daily closing gold price.

Rhodium rose to 8,050 USD/t oz. on December 2, 2025, up 0.94% from the previous day. Over the past month, Rhodium's price has fallen 1.23%, but it is still 75.96% higher than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Rhodium - values, historical data, forecasts and news - updated on December of 2025.

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.

The size of the Gold Market was valued at USD 3.2 Trillion in 2023 and is projected to reach USD 4.5 Trillion by 2032, with an expected CAGR of 7.38% during the forecast period. Recent developments include: March 2023: Pan American Silver Corporation acquired all the issued and outstanding common shares of Yamana Gold Inc., as part of the arrangement, which includes its mines and increased the geographical operations of the company in Latin America., February 2023: Barrick Gold, the world's second-biggest gold producer, announced a 10% increase in attributable proved and probable gold mineral reserves to 76 million ounces net of depletion in 2022 while maintaining current reserves.. Key drivers for this market are: Demand for Gold in the form of Jewelry and Long-term Savings, Increasing Consumption in High-End Electronics Applications; Other Drivers. Potential restraints include: Declining Ore Grades and Other Technical Challenges, Other Restraints. Notable trends are: Jewelry Segment to Dominate the Demand.

Banking and stock markets consider gold to be an important component of their economic and financial status. There are various factors that influence the gold price trend and its fluctuations. Accurate and reliable prediction of the gold price is an essential part of financial and portfolio management. Moreover, it could provide insights about potential buy and sell points in order to prevent financial damages and reduce the risk of investment. In this paper, different architectures of deep neural network (DNN) have been proposed based on long short-term memory (LSTM) and convolutional-based neural networks (CNN) as a hybrid model, along with automatic parameter tuning to increase the accuracy, coefficient of determination, of the forecasting results. An illustrative dataset from the closing gold prices for 44 years, from 1978 to 2021, is provided to demonstrate the effectiveness and feasibility of this method. The grid search technique finds the optimal set of DNNs’ parameters. Furthermore, to assess the efficiency of DNN models, three statistical indices of RMSE, RMAE, and coefficient of determination (R2), were calculated for the test set. Results indicate that the proposed hybrid model (CNN-Bi-LSTM) outperforms other models in total bias, capturing extreme values and obtaining promising results. In this model, CNN is used to extract features of input dataset. Furthermore, Bi-LSTM uses CNN’s outputs to predict the daily closing gold price.

Banking and stock markets consider gold to be an important component of their economic and financial status. There are various factors that influence the gold price trend and its fluctuations. Accurate and reliable prediction of the gold price is an essential part of financial and portfolio management. Moreover, it could provide insights about potential buy and sell points in order to prevent financial damages and reduce the risk of investment. In this paper, different architectures of deep neural network (DNN) have been proposed based on long short-term memory (LSTM) and convolutional-based neural networks (CNN) as a hybrid model, along with automatic parameter tuning to increase the accuracy, coefficient of determination, of the forecasting results. An illustrative dataset from the closing gold prices for 44 years, from 1978 to 2021, is provided to demonstrate the effectiveness and feasibility of this method. The grid search technique finds the optimal set of DNNs’ parameters. Furthermore, to assess the efficiency of DNN models, three statistical indices of RMSE, RMAE, and coefficient of determination (R2), were calculated for the test set. Results indicate that the proposed hybrid model (CNN-Bi-LSTM) outperforms other models in total bias, capturing extreme values and obtaining promising results. In this model, CNN is used to extract features of input dataset. Furthermore, Bi-LSTM uses CNN’s outputs to predict the daily closing gold price.

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.

Iron Ore rose to 106.94 USD/T on December 1, 2025, up 2.00% from the previous day. Over the past month, Iron Ore's price has risen 1.04%, and is up 1.54% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Iron Ore - values, historical data, forecasts and news - updated on December of 2025.

Rhodium price data, historical values, forecasts, and news provided by Money Metals Exchange. Rhodium prices and trends updated regularly to provide accurate market insights.