Assessing Model Prediction Confidence

Requirements Clarification & Assessment

1. Objective Understanding:

   • Evaluate the uncertainty of a time-series forecasting model used for predicting stock prices.
   • Quantify the uncertainty to better understand the model's performance.

2. Data Context:

   • Historical data of predicted versus actual stock prices is available.
   • The model is already developed and presumably trained on past data.

3. Uncertainty Types:

   • Prediction Intervals (PIs): Focus on the uncertainty in future observable values.
   • Confidence Intervals (CIs): Reflect uncertainty in parameter estimates or model predictions.

4. Performance Metrics:

   • Need to assess both the accuracy and the uncertainty of predictions.
   • Metrics like Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and others could be relevant.

5. Evaluation Requirements:

   • Quantify uncertainty using statistical methods.
   • Provide insights into the reliability of future predictions.

6. Constraints & Considerations:

   • Time constraints for the analysis.
   • Availability and quality of historical data.
   • Model complexity and interpretability.