Importance of AI to Predict Live Commodity Prices

Artificial intelligence is not a new concept to briefly introduce, as it is becoming more prevalent in everyone's daily lives through smartphones and other technological equipment. It also includes algorithms that can provide outcomes that are highly dependent on the input data provided—most used to forecast live commodity prices.

AI in Commodity Forecasting

In commodity price forecasting, artificial intelligence typically employs ML - machine learning. This strategy aids in the automatic breakdown of structured and unstructured data and the creation of models to anticipate live commodity prices with minimal human interaction. Things that are normally unseen to the naked eye can be revealed, allowing producers to prepare ahead of time, traders to estimate pricing, and buyers to plan more strategic procurement.

AI-Based Forecasting – Live Commodity Prices

Traditional forecasting approaches focus on a quantitative and qualitative evaluation of supply and demand-side elements, as well as statistical models based on univariate methodologies and historical pricing data. These techniques, on the other hand, fail to capture all market variables and even perform poorly for longer time horizon forecasts (weekly/monthly).

On the other hand, AI/ML-based forecasting models can provide more accurate projections across regions and exchanges over longer time periods. These algorithms can also process massive amounts of historical data to find hidden trends, helping firms make more informed and successful business decisions. Among the benefits of AI-based commodity forecasting are:

• The ability to deal with high price volatility.

• Capability to integrate multiple predictors from disparate sources.

• The ability to forecast accurately over multiple forecast horizons (daily, weekly, monthly).

• Model Interpretability, or the ability to grasp the significance of a variable

However, artificial intelligence aids in the continued use and development of cutting-edge solutions to create the best forecasts possible across various commodities and prediction horizons. And it has become the most popular digital method for predicting commodity futures prices.

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