Aluminum rose to 3,275.20 USD/T on March 27, 2026, up 0.81% from the previous day. Over the past month, Aluminum's price has risen 2.53%, and is up 28.40% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Aluminum - values, historical data, forecasts and news - updated on March of 2026.

Aluminium prices from 2014 to 2022 for Time Series Analysis. Good practice for univariate time series analysis.

Monthly Aluminum prices measured in USD per metric tonne (mt).

This dataset provides values for ALUMINUM reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

This time I deided to pay attention on the changes in metal prices within last 30 year. The most popular and interesting in visualization metals prices were tacken: Gold, Aluminium, Silver, Uranium and Nickel Don't forget to check out my previous "Price Changes within last 30 Years" datasets: 🌽 Cerial Prices Changes Within Last 30 Years ☕Coffee, Rice and Beef Prices Changes for 30 Years

This dataset is created to enable data-driven forecasting and explainability for short-term price movements of LME Aluminum Spot Prices by combining:

• Metal spot prices
• Global equity & commodity indices
• Mining & aluminum company stock prices
• Freight & shipping stress indicator
• Physical aluminum inventory levels

The objective is to support deep learning, time-series modeling, and feature attribution research in commodity markets.

This dataset is structured as a SQLite3 database so that users can directly plug it into ML / DL pipelines without CSV wrangling.

🎯 Primary Use Case

Forecast 1-week ahead LME Aluminum spot prices using multi-source market signals and perform model explainability to understand market drivers.

🗂️ Database Structure

The database contains four core tables:

🪙 Target Variable

LME Aluminum Spot Price

• Daily spot price of aluminum traded on the LME
• Acts as the prediction target

Source: MetalAPI (TopCable)

🌍 Market & Equity Indicators

These indices and stocks capture macro, mining, risk sentiment, and industrial demand signals.

Source: Google Finance

🏗️ Physical Supply Indicator

London Metal Exchange Inventory Data

Daily LME aluminum inventory stock levels.

Why important:

Inventory acts as real supply pressure signal — often leading price movement.

Source: WestMetall

🚢 Global Trade & Demand Indicator

Baltic Dry Index

Captures:

• Global shipping demand
• Industrial & commodity trade flow
• Leading signal for metal demand

Source: Investing.com

🧪 Potential Research & ML Use Cases

Beyond forecasting, this dataset enables:

1. Time-Series Forecasting

• LSTM / GRU / Temporal CNN / Transformer
• Multi-variate sequence modeling
• 7-day ahead forecasting

2. Feature Explainability

• SHAP / Attention visualization
• Identify which market signal moved aluminum price
• Inventory vs equity vs freight vs volatility impact

3. Causality & Lead-Lag Analysis

• Granger causality
• Cross-correlation heatmaps
• Discover leading indicators

4. Regime Detection

• Detect bull / bear / supply shock regimes
• Clustering on macro signals

5. Synthetic Trading Strategy Backtests

• Build signal-based aluminum trading strategy
• Use indices as leading signals

6. Supply Chain Intelligence Modeling

• Map inventory + freight + energy → price

7. Representation Learning

• Learn latent embeddings of commodity market states

8. Transfer Learning

• Use learned embeddings for Copper, Zinc, Nickel forecasting

⏱️ Time Range

01 Jan 2015 → 05 Feb 2026 Daily frequency

🧹 Preprocessing Notes

• All dates are aligned to daily frequency
• Missing market holidays handled via forward fill
• Ready for direct modeling from SQLite

⚙️ How to Use

import sqlite3
import pandas as pd

conn = sqlite3.connect("autonomous-metal-db.db")

spot = pd.read_sql("SELECT * FROM `lme-aluminum-spot-prices`", conn)
indices = pd.read_sql("SELECT * FROM `google-finance-index`", conn)
inventory = pd.read_sql("SELECT * FROM `lme-aluminum-daily-inventory`", conn)
bdi = pd.read_sql("SELECT * FROM `baltic-dry-index`", conn)

👤 Who Is This Dataset For?

• Quant researchers
• Commodity ...

Global Aluminum Prices - Historical chart and current data through 2026.

CN: Transaction Price: Aluminum: AOO: Dalian data was reported at 15,980.000 RMB/Ton in Nov 2019. This records a decrease from the previous number of 16,200.000 RMB/Ton for Oct 2019. CN: Transaction Price: Aluminum: AOO: Dalian data is updated monthly, averaging 16,000.000 RMB/Ton from Dec 1995 (Median) to Nov 2019, with 274 observations. The data reached an all-time high of 21,850.000 RMB/Ton in Oct 2006 and a record low of 11,500.000 RMB/Ton in Nov 2015. CN: Transaction Price: Aluminum: AOO: Dalian data remains active status in CEIC and is reported by Price Monitoring Center, NDRC. The data is categorized under China Premium Database’s Price – Table CN.PG: Aluminum Price.

Bauxite and Aluminum Import Prices - Historical chart and current data through 2025.

CN: Transaction Price: Aluminum: AOO: Jinan data was reported at 14,150.000 RMB/Ton in Nov 2019. This records a decrease from the previous number of 14,250.000 RMB/Ton for Oct 2019. CN: Transaction Price: Aluminum: AOO: Jinan data is updated monthly, averaging 15,500.000 RMB/Ton from Dec 1995 (Median) to Nov 2019, with 268 observations. The data reached an all-time high of 24,466.670 RMB/Ton in Feb 2004 and a record low of 10,200.000 RMB/Ton in Jan 2016. CN: Transaction Price: Aluminum: AOO: Jinan data remains active status in CEIC and is reported by Price Monitoring Center, NDRC. The data is categorized under China Premium Database’s Price – Table CN.PG: Aluminum Price.

This dataset contains historical price data for seven essential metals traded on the Multi Commodity Exchange (MCX) India: Gold, Silver, Lead, Zinc, Copper, Nickel, and Aluminum. The data is meticulously collected to support prediction models, trend analysis, and statistical exploration of metal price movements.

The dataset includes: - Daily price data for 7 metals - Open price, high/low values, and closing prices - Data across multiple periods, useful for preliminary exploration, model training, and analysis

Description for each column in the dataset: 1. Date: The date on which the market data was recorded (format: DD-MM-YYYY). 2. Price: The closing price of Copper on the given date, reflecting the last traded price of the day. 3. Open: The opening price of Copper at the start of trading on the given date. 4. High: The highest price Copper reached during the trading day. 5. Low: The lowest price Copper traded at during the day. 6. Vol. (Volume): The total volume of Copper traded on the given day, typically in thousands (K). 7. Change %: The percentage change in the closing price from the previous trading day.

Monthly aluminum spot price forecasts for the period Aug, 2016 - Oct, 2024. Forecasts were obtained from various Bayesian-based econometric models. Stored in tab format. Results of own estimations. y_hat_rel contains aluminum price forecasts from various econometric models based on released data (i.e., real-time forecasting approach).y_hat_rev - based on revised data.y_hat_fx - based on initial data, but with fx rates replaced by alternatives.y_hat_last - when end-of-month price (not average) was forecasted.See README file attached.

LME Index fell to 1 Index Points on December 18, 2025, down 99.98% from the previous day. Over the past month, LME Index's price has remained flat, but it is still 99.97% lower than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. LME Index - values, historical data, forecasts and news - updated on March of 2026.

Monthly and long-term alumina price data (US$/mt): historical series and analyst forecasts curated by FocusEconomics.

Daily updated dataset showing Hindalco aluminium price movements including upgrade, down, and stable statuses with historical tracking in HTML table format.

This dataset contains raw mechanical data (strain, stress and strain rate curvesand initial temperatures) for compression experiments performed on aluminum alloy 6061-T6using the NIST pulse-heated Kolsky Bar at strain rates between 2400 1/s and 2700 1/s and initialtemperatures from 23 °C to 527 °C and heating times ranging between 0.2 s and 3.5 s. At this time,the thermal history of the specimen during heating is not included in the dataset but this informationmay be included in a future update. The pulse-heated Kolsky bar method is described in reference [1].Additional information regarding the temperature measurement methods specific to aluminum are found inreference [2] as well as a supplementary included in this data publication [3]. This datasetincludes measurements of right-circular cylindrical samples measuring 6 mm diameter by 3 mm thick,fabricated by conventional machining. The effect of the surface condition on the thermographic temperature measurements and the temperature uncertainty calculations are described in reference [3], which is included in this dataset as metadata.[1] Mates, S.P., et al., A Pulse-Heated Kolsky Bar Technique for Measuring the Flow Stress of Metals at High Loading and Heating Rates. Experimental Mechanics, 2008. 48(6): p. 799-807.https://dx.doi.org/10.1007/s11340-008-9137-1[2] Lopez-Hawa, H., et al., On the Mechanical Response of Aluminum Alloy 6061-T6 under Extreme Strains and Strain Rates, and Rapid Heating. Manufacturing Letters, 2022. 33: p. 292-301. https://dx.doi.org/10.1016/j.mfglet.2022.07.036[3] Aluminum_insitu_calibration.docx (included in this dataset)

This dataset contains monthly historical prices of 10 different commodities from January 1980 to April 2023. The data was collected from the Alpha Vantage API using Python. The commodities included in the dataset are WTI crude oil, cotton, natural gas, coffee, sugar, aluminum, Brent crude oil, corn, copper, and wheat. Prices are reported in USD per unit of measurement for each commodity. The dataset contains 520 rows and 12 columns, with each row representing a monthly observation of the prices of the 10 commodities. The 'All_Commodities' column is new.

WTI: WTI crude oil price per unit of measurement (USD). COTTON: Cotton price per unit of measurement (USD). NATURAL_GAS: Natural gas price per unit of measurement (USD). ALL_COMMODITIES: A composite index that represents the average price of all 10 commodities in the dataset, weighted by their individual market capitalizations. Prices are reported in USD per unit of measurement. COFFEE: Coffee price per unit of measurement (USD). SUGAR: Sugar price per unit of measurement (USD). ALUMINUM: Aluminum price per unit of measurement (USD). BRENT: Brent crude oil price per unit of measurement (USD). CORN: Corn price per unit of measurement (USD). COPPER: Copper price per unit of measurement (USD). WHEAT: Wheat price per unit of measurement (USD).

Note that some values are missing in the dataset, represented by NaN. These missing values occur for some of the commodities in the earlier years of the dataset.

It may be useful for time series analysis and predictive modeling.

NaN values were included so that you as a Data Scientist can get some practice on dealing with NaN values.

https://www.alphavantage.co/documentation/

CN: Transaction Price: Aluminum: AOO: Xian data was reported at 14,730.000 RMB/Ton in Nov 2019. This records an increase from the previous number of 14,390.000 RMB/Ton for Oct 2019. CN: Transaction Price: Aluminum: AOO: Xian data is updated monthly, averaging 15,500.000 RMB/Ton from Dec 1995 (Median) to Nov 2019, with 267 observations. The data reached an all-time high of 22,716.670 RMB/Ton in May 2006 and a record low of 10,850.000 RMB/Ton in Nov 2015. CN: Transaction Price: Aluminum: AOO: Xian data remains active status in CEIC and is reported by Price Monitoring Center, NDRC. The data is categorized under China Premium Database’s Price – Table CN.PG: Aluminum Price.

A method of improving the burn rate and ignitability of aluminium fuel particles, and a thus modified aluminium fuel for use in propellant and explosive compositions and pyrotechnic charges. Aluminium fuel particles are treated with an aqueous solution of hydrofluoric acid and a fluoride and/or complex fluoride salt of an alkali metal and/or alkaline earth metal to form a surface layer of a fluoride complex bound to the aluminium fuel particle.

This dataset is about: (Table II) Production rates of beryllium-10 and aluminium-26 nuclides in samples from three glacial deposits in the McMurdo Dry Valleys. Please consult parent dataset @ https://doi.org/10.1594/PANGAEA.817199 for more information.