RSI

Time Decay refers to the diminishing impact of older data in time series analysis, crucial for adapting models to recent trends.

Retrieval-Augmented Generation (RAG) refers to a methodology that combines retrieval with generation to improve AI model outputs.

Root Mean Squared Error (RMSE) measures the average magnitude of errors in a set of predictions, useful for evaluating forecast accuracy.

Quad refers to a methodology used in AI-based commodity price forecasting, involving data segmentation and model optimization.

Mean Absolute Percentage Error (MAPE) measures forecasting accuracy by expressing prediction errors as a percentage of actual values.

Mean Absolute Error (MAE) measures the average magnitude of errors in a set of predictions, without considering their direction.

Hit Rate measures the proportion of correct predictions in a forecasting model, indicating its accuracy.

Forecast Horizon refers to the time span over which predictions or forecasts are made, crucial for planning and decision-making.

Forecast Confidence measures the reliability and accuracy of a commodity price prediction, indicating the level of certainty in the forecasted outcomes.

Explainable AI (XAI) refers to methods and techniques that make AI model outputs understandable to humans, crucial for transparency and trust.

Ex-Post Analysis refers to the evaluation of forecasting accuracy by comparing predicted values with actual outcomes after the fact.

Event-Based Forecasting refers to a methodology that uses specific events to predict changes in commodity prices, focusing on the impact of these events rather than historical trends.

Ensemble Modeling refers to the combination of multiple models to improve the accuracy and robustness of predictions.

Backtesting refers to the process of testing a predictive model or strategy using historical data to evaluate its accuracy and reliability.

Structural breaks refer to sudden changes in a time series data pattern, impacting the stability and predictability of models.