Gold rose to 3,642.91 USD/t.oz on September 9, 2025, up 0.20% from the previous day. Over the past month, Gold's price has risen 8.97%, and is up 44.64% 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 September of 2025.

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

Silver rose to 41.30 USD/t.oz on September 8, 2025, up 0.82% from the previous day. Over the past month, Silver's price has risen 9.77%, and is up 45.73% 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 September of 2025.

Indonesia Retail Price: Gold: 24 Carat data was reported at 539,218.000 IDR in Nov 2017. This records a decrease from the previous number of 539,520.000 IDR for Oct 2017. Indonesia Retail Price: Gold: 24 Carat data is updated monthly, averaging 85,000.000 IDR from May 1987 (Median) to Nov 2017, with 367 observations. The data reached an all-time high of 560,000.000 IDR in Aug 2016 and a record low of 21,350.000 IDR in Jun 1990. Indonesia Retail Price: Gold: 24 Carat data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Global Database’s Indonesia – Table ID.PC001: Retail Price: By Major Commodities.

Copper fell to 4.50 USD/Lbs on September 9, 2025, down 0.02% from the previous day. Over the past month, Copper's price has risen 1.40%, and is up 11.18% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Copper - values, historical data, forecasts and news - updated on September of 2025.

Vietnam Gold Price Index: YoY: Hanoi data was reported at 3.370 % in Jun 2018. This records a decrease from the previous number of 4.000 % for May 2018. Vietnam Gold Price Index: YoY: Hanoi data is updated monthly, averaging 5.635 % from Aug 2008 (Median) to Jun 2018, with 118 observations. The data reached an all-time high of 71.470 % in Dec 2009 and a record low of -25.020 % in Jan 2014. Vietnam Gold Price Index: YoY: Hanoi data remains active status in CEIC and is reported by Hanoi Statistical Office. The data is categorized under Global Database’s Vietnam – Table VN.I030: Gold Price Index: MoM & YoY Growth.

Ghana Commodity Price: Gold data was reported at 1,281.100 USD/Fine oz in Jun 2018. This records a decrease from the previous number of 1,303.000 USD/Fine oz for May 2018. Ghana Commodity Price: Gold data is updated monthly, averaging 1,182.900 USD/Fine oz from Dec 2003 (Median) to Jun 2018, with 175 observations. The data reached an all-time high of 1,770.130 USD/Fine oz in Aug 2011 and a record low of 384.730 USD/Fine oz in May 2004. Ghana Commodity Price: Gold data remains active status in CEIC and is reported by Bank of Ghana. The data is categorized under Global Database’s Ghana – Table GH.P001: Commodity Price.

Bullion Price: Monthly Average: Mumbai: Gold: Standard data was reported at 84,995.000 INR/10 g in Feb 2025. This records an increase from the previous number of 79,079.000 INR/10 g for Jan 2025. Bullion Price: Monthly Average: Mumbai: Gold: Standard data is updated monthly, averaging 9,691.000 INR/10 g from Apr 1990 (Median) to Feb 2025, with 419 observations. The data reached an all-time high of 84,995.000 INR/10 g in Feb 2025 and a record low of 3,285.000 INR/10 g in Jul 1990. Bullion Price: Monthly Average: Mumbai: Gold: Standard data remains active status in CEIC and is reported by Reserve Bank of India. The data is categorized under Global Database’s India – Table IN.PG002: Memo Items: Bullion Price.

Accurate prediction of gold prices is crucial for investment decision-making and national risk management. The time series data of gold prices exhibits random fluctuations, non-linear characteristics, and high volatility, making prediction extremely challenging. Various methods, from classical statistics to machine learning techniques like Random Forests, Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN), have achieved high accuracy, but they also have inherent limitations. To address these issues, a model that combines Temporal Convolutional Networks (TCN) with Query (Q) and Keys (K) attention mechanisms (TCN-QV) is proposed to enhance the accuracy of gold price predictions. The model begins by employing stacked dilated causal convolution layers within the TCN framework to effectively extract temporal features from the sequence data. Subsequently, an attention mechanism is introduced to enable adaptive weight distribution according to the information features. Finally, the predicted results are generated through a dense layer. This method is used to predict the time series data of gold prices in Shanghai. The optimized model demonstrates a substantial improvement in Mean Absolute Error (MAE) compared to the baseline model, achieving reductions of approximately 5.47% in the least favorable case and up to 33.69% in the most favorable scenario across four experimental datasets. Additionally, the model is tested across different time steps and shows satisfactory performance in long sequence predictions. To validate the necessity of the model components, this paper conducts ablation experiments to confirm the significance of each segment.

Turkey Gold Selling Price: Free Market: Republic data was reported at 1,600.000 TRY/Unit in Oct 2018. This records a decrease from the previous number of 1,673.750 TRY/Unit for Sep 2018. Turkey Gold Selling Price: Free Market: Republic data is updated monthly, averaging 16.813 TRY/Unit from Dec 1977 (Median) to Oct 2018, with 491 observations. The data reached an all-time high of 1,673.750 TRY/Unit in Sep 2018 and a record low of 0.001 TRY/Unit in Dec 1977. Turkey Gold Selling Price: Free Market: Republic data remains active status in CEIC and is reported by Central Bank of the Republic of Turkey. The data is categorized under Global Database’s Turkey – Table TR.P001: Gold Price.

Vietnam Gold Price Index: 2009=100: HCMC data was reported at 175.530 2009=100 in Nov 2015. This records a decrease from the previous number of 176.930 2009=100 for Oct 2015. Vietnam Gold Price Index: 2009=100: HCMC data is updated monthly, averaging 189.260 2009=100 from Nov 2009 (Median) to Nov 2015, with 73 observations. The data reached an all-time high of 246.450 2009=100 in Oct 2012 and a record low of 127.540 2009=100 in Nov 2009. Vietnam Gold Price Index: 2009=100: HCMC data remains active status in CEIC and is reported by Ho Chi Minh City Statistics Office. The data is categorized under Global Database’s Vietnam – Table VN.I029: Gold Price Index. Rebased from 2009p to 2014p. Replacement Series ID: 375313157.

Iron Ore fell to 104.49 USD/T on September 5, 2025, down 0.04% from the previous day. Over the past month, Iron Ore's price has risen 3.54%, and is up 14.06% 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 September 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.

This dataset tracks annual reduced-price lunch eligibility from 2000 to 2023 for Gold Burg School vs. Texas and Gold Burg Independent School District

Aluminum rose to 2,614.40 USD/T on September 8, 2025, up 0.32% from the previous day. Over the past month, Aluminum's price has risen 1.16%, and is up 11.23% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Aluminum - values, historical data, forecasts and news - updated on September of 2025.

Second-Hand Fashion Dataset

Update Sep. 19th, 2024

• Some problematic and duplicate images have been removed from version 2.- All "gold dataset" data from station1 and station3 has been moved to a single test100 folder.- JSON errors have been fixed - all JSON files should be parsed correctly now.

The new dataset has 31,638 items (+ about 100 items in test100 folder) instead of the 31,997 items in version 2.

Overview

The dataset originates from projects focused on the sorting of used clothes within a sorting facility. The primary objective is to classify each garment into one of several categories to determine its ultimate destination: reuse, reuse outside Sweden (export), recycling, repair, remake, or thermal waste.

The dataset has 31,638 clothing items, a massive update from the 3,000 items in version 1. The dataset collection started under the Vinnova funded project "AI for resource-efficient circular fashion" in Spring, 2022 and involves collaboration among three institutions: RISE Research Institutes of Sweden AB, Wargön Innovation AB, and Myrorna AB. The dataset has received further support through the EU project, CISUTAC (cisutac.eu).

Project page

• Webpage: second-hand-fashion- Contact: farrukh.nauman@ri.se

Dataset Details

• The dataset contains 31,638 clothing items, each with a unique item ID in a datetime format. The items are divided into three stations: station1, station2, and station3. The station1 and station2 folders contain images and annotations from Wargön Innovation AB, while the station3 folder contains data from Myrorna AB. Each clothing item has three images and a JSON file containing annotations.

• Three images are provided for each clothing item: 1. Front view. 2. Back view. 3. Brand label close-up. About 4000-5000 brand images are missing because of privacy concerns: people's hands, faces, etc. Some clothing items did not have a brand label to begin with.

• Image resolutions are primarily in two sizes: 1280x720 and 1920x1080. The background of the images is a table that used a measuring tape prior to January 2023, but later images have a square grid pattern with each square measuring 10x10 cm.

• Each JSON file contains a list of annotations, some of which require nuanced interpretation (see labels.py for the options): - usage: Arguably the most critical label, usage indicates the garment's intended pathway. Options include 'Reuse,' 'Repair,' 'Remake,' 'Recycle,' 'Export' (reuse outside Sweden), and 'Energy recovery' (thermal waste). About 99% of the garments fall into the 'Reuse,' 'Export,' or 'Recycle' categories. - trend: This field refers to the general style of the garment, not a time-dependent trend as in some other datasets (e.g., Visuelle 2.0). It might be more accurately labeled as 'style.' - material: Material annotations are mostly based on the readings from a Near Infrared (NIR) scanner and in some cases from the garment's brand label. - Damage-related attributes include: - condition (1-5 scale, 5 being the best) - pilling (1-5 scale, 5 meaning no pilling) - stains, holes, smell (each with options 'None,' 'Minor,' 'Major'). Note: 'holes' and 'smell' were introduced after November 17th, 2022, and stains previously only had 'Yes'/'No' options. For station1 and station2, we introduced additional damage location labels to assist in damage detection:

    "damageimage": "back",
    "damageloc": "bottom left",
    "damage": "stain ",
    "damage2image": "front",
    "damage2loc": "None",
    "damage2": "",
    "damage3image": "back",
    "damage3loc": "bottom right",
    "damage3": "stain"
  
  Taken from `labels_2024_04_05_08_47_35.json` file. Additionally, we annotated a few hundred images with bounding box annotations that we aim to release at a later date.  - `comments`: The comments field is mostly empty, but sometimes contains important information about the garment, such as a detailed text description of the damage. 
  

• Whenever possible, ISO standards have been followed to define these attributes on a 1-5 scale (e.g., pilling).

• Gold dataset: 100 garments were annotated multiple times by different annotators for annotator agreement comparisons. These 100 garments are placed inside a separate folder test100.

• The data has been annotated by a group of expert second-hand sorters at Wargön Innovation AB and Myrorna AB.

• Some attributes, such as price, should be considered with caution. Many distinct pricing models exist in the second-hand industry: - Price by weight - Price by brand and demand (similar to first-hand fashion) - Generic pricing at a fixed value (e.g., 1 Euro or 10 SEK) Wargön Innovation AB does not set the prices in practice and their prices are suggestive only (station1 and station2). Myrorna AB (station3), in contrast, does resale and sets the prices.

Comments

• We received feedback on our version 1 that some images were too blurry or had poor lighting. The image quality has slightly improved, but largely remains similar to release 1. - Some users did not prefer a tar.gz format that we uploaded in version 1 of the dataset. We have now switched to .zip for convenience.- Extra care was taken not to leak personal information. This is why you will not see any entries for annotator attribute in the JSON files in station1/sep2023 since people used their real names. Since then, we used internally assigned IDs. - Many brand images contained people's hands, faces, or other personal information. We have removed about 4000-5000 brand images for privacy reasons. - Please inform us immediately if you find any personal information revelations in the dataset: - Farrukh Nauman (RISE AB): farrukh.nauman@ri.se, - Susanne Eriksson (Wargön Innovation AB): susanne.eriksson@wargoninnovation.se, - Gabriella Engstrom (Wargön Innovation AB): gabriella.engstrom@wargoninnovation.se.

We went through 100k images four times to ensure no personal information is leaked, but we are human and can make mistakes.

Partners

The data collection for this dataset has been carried out in collaboration with the following partners:

1. RISE Research Institutes of Sweden AB: RISE is a leading research institute dedicated to advancing innovation and sustainability across various sectors, including fashion and textiles.

2. Wargön Innovation AB: Wargön Innovation is an expert in sustainable and circular fashion solutions, contributing valuable insights and expertise to the dataset creation.

3. Myrorna AB: Myrorna is Sweden's oldest chain of stores for collecting clothes and furnishings that can be reused.

License

CC-BY 4.0. Please refer to the LICENSE file for more details.

Acknowledgments

This dataset was made possible through the collaborative efforts of RISE Research Institutes of Sweden AB, Wargön Innovation AB, and Myrorna AB, with funding from Vinnova and support from the EU project CISUTAC. We extend our gratitude to all the expert second-hand sorters and annotators who contributed their expertise to this project.

Accurate prediction of gold prices is crucial for investment decision-making and national risk management. The time series data of gold prices exhibits random fluctuations, non-linear characteristics, and high volatility, making prediction extremely challenging. Various methods, from classical statistics to machine learning techniques like Random Forests, Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN), have achieved high accuracy, but they also have inherent limitations. To address these issues, a model that combines Temporal Convolutional Networks (TCN) with Query (Q) and Keys (K) attention mechanisms (TCN-QV) is proposed to enhance the accuracy of gold price predictions. The model begins by employing stacked dilated causal convolution layers within the TCN framework to effectively extract temporal features from the sequence data. Subsequently, an attention mechanism is introduced to enable adaptive weight distribution according to the information features. Finally, the predicted results are generated through a dense layer. This method is used to predict the time series data of gold prices in Shanghai. The optimized model demonstrates a substantial improvement in Mean Absolute Error (MAE) compared to the baseline model, achieving reductions of approximately 5.47% in the least favorable case and up to 33.69% in the most favorable scenario across four experimental datasets. Additionally, the model is tested across different time steps and shows satisfactory performance in long sequence predictions. To validate the necessity of the model components, this paper conducts ablation experiments to confirm the significance of each segment.

This dataset tracks annual reduced-price lunch eligibility from 1999 to 2023 for Gold Hill Elementary School vs. South Carolina and York 04 School District

This dataset tracks annual reduced-price lunch eligibility from 2001 to 2023 for Gold Trail vs. California and Gold Trail Union Elementary School District

About This Dataset: Copper Prices and Market Trends

    This dataset provides **insights into copper prices**, including current rates, historical trends, and key factors affecting price fluctuations. Copper is essential in **construction**, **electronics**, and **transportation** industries. Investors, traders, and analysts use accurate copper price data to guide decisions related to **trading**, **futures**, and **commodity investments**.

    ### **Key Features of the Dataset**

    #### **Live Market Data and Updates**
    Stay updated with the latest **copper price per pound** in USD. This data is sourced from exchanges like the **London Metal Exchange (LME)** and **COMEX**. Price fluctuations result from **global supply-demand shifts**, currency changes, and geopolitical factors.

    #### **Interactive Copper Price Charts**
    Explore **dynamic charts** showcasing real-time and historical price movements. These compare copper with **gold**, **silver**, and **aluminium**, offering insights into **market trends** and inter-metal correlations.

    ### **Factors Driving Copper Prices**

    #### **1. Supply and Demand Dynamics**
    Global copper supply is driven by mining activities in regions like **Peru**, **China**, and the **United States**. Disruptions in production or policy changes can cause **supply shocks**. On the demand side, **industrial growth** in countries like **India** and **China** sustains demand for copper.

    #### **2. Economic and Industry Trends**
    Copper prices often reflect **economic trends**. The push for **renewable energy** and **electric vehicles** has boosted long-term demand. Conversely, economic downturns and **inflation** can reduce demand, lowering prices.

    #### **3. Impact of Currency and Trade Policies**
    As a globally traded commodity, copper prices are influenced by **currency fluctuations** and **tariff policies**. A strong **US dollar** typically suppresses copper prices by increasing costs for international buyers. Trade tensions can also disrupt **commodity markets**.

    ### **Applications and Benefits**

    This dataset supports **commodity investors**, **traders**, and **industry professionals**:

    - **Investors** forecast price trends and manage **investment risks**. 
    - **Analysts** perform **market research** using price data to assess **copper futures**. 
    - **Manufacturers** optimize supply chains and **cost forecasts**.

    Explore more about copper investments on **Money Metals**:

    - [**Buy Copper Products**](https://www.moneymetals.com/buy/copper) 
    - [**95% Copper Pennies (Pre-1983)**](https://www.moneymetals.com/pre-1983-95-percent-copper-pennies/4) 
    - [**Copper Buffalo Rounds**](https://www.moneymetals.com/copper-buffalo-round-1-avdp-oz-999-pure-copper/297)

    ### **Copper Price Comparisons with Other Metals**

    Copper prices often correlate with those of **industrial** and **precious metals**:

    - **Gold** and **silver** are sensitive to **inflation** and currency shifts. 
    - **Iron ore** and **aluminium** reflect changes in **global demand** within construction and manufacturing sectors.

    These correlations help traders develop **hedging strategies** and **investment models**.

    ### **Data Variables and Availability**

    Key metrics include:

    - **Copper Price Per Pound:** The current market price in USD. 
    - **Copper Futures Price:** Data from **COMEX** futures contracts. 
    - **Historical Price Trends:** Long-term movements, updated regularly. 

    Data is available in **CSV** and **JSON** formats, enabling integration with analytical tools and platforms.

    ### **Conclusion**

    Copper price data is crucial for **monitoring global commodity markets**. From **mining** to **investment strategies**, copper impacts industries worldwide. Reliable data supports **risk management**, **planning**, and **economic forecasting**.

    For more tools and data, visit the **Money Metals** [Copper Prices Page](https://www.moneymetals.com/copper-prices).