The Out-of-Sample Forecasting Accuracy Tool is an advanced interactive tool designed to evaluate the accuracy of forecasting models by comparing predicted values with actual out-of-sample data. This tool calculates common accuracy metrics such as Mean Absolute Error (MAE) , Mean Squared Error (MSE) , and Root Mean Squared Error (RMSE) , providing users with a quantitative measure of model performance.

Key Features:

1. Dynamic Input : Users can input actual observed values and forecasted values as comma-separated lists.
2. Accuracy Metrics : The tool computes and displays key metrics: MAE, MSE, and RMSE.
3. Interactive Visualization : A scatter plot compares actual vs. forecasted values, with a 45-degree reference line to highlight deviations.
4. PDF Download : Export the accuracy results and visualization as a PDF for sharing or reporting purposes.
5. Modern Design : Clean and stylish layout with vibrant colors and clear typography for better readability.
6. Self-Contained : The tool operates within its own container, ensuring it doesn’t interfere with the page header or footer.

Use Cases:

• Model Evaluation : Analysts can assess the performance of forecasting models such as ARIMA, Holt-Winters, or machine learning models.
• Comparative Analysis : Researchers can compare the accuracy of multiple forecasting methods using standardized metrics.
• Reporting : Professionals can export accuracy results and visualizations to include in reports or presentations.

How It Works:

1. Introduction : Out-of-sample forecasting accuracy measures how well a model predicts unseen data. By comparing actual observed values with forecasted values, this tool calculates key metrics:
   • MAE : Measures the average absolute error between actual and forecasted values.
   • MSE : Penalizes larger errors more heavily by squaring the differences.
   • RMSE : The square root of MSE, providing error magnitude in the original units of the data.
2. Input Fields : Users enter actual observed values and forecasted values as comma-separated lists.
3. Visualization : The tool generates a scatter plot comparing actual vs. forecasted values, with a 45-degree reference line.
4. Export : Users can download the accuracy results and visualization as a PDF for further use.