Time Series Analysis and Prediction of Healthcare Trends Using Gaussian Process Regression

Preprocess and structure time-series data for accurate modeling.

Apply differencing techniques to ensure stationarity.

Use Gaussian Process Regression for trend prediction.

Design custom kernels for periodic and non-linear patterns.

Generate predictions with confidence intervals for uncertainty analysis.

Evaluate models using R², MAE, and RMSE metrics.

Visualize trends, residuals, and prediction intervals effectively.

Revert different predictions for meaningful insights.

Conduct residual analysis to minimize errors.

Create a practical workflow for real-world time-series projects.