Added a new notebook, interpolation_v0.3 4.ipynb, which demonstrates and compares imputation techniques for missing risk-factor data. It specifically explores Linear Interpolation against Univariate and Multivariate Kalman filters, evaluating their performance across different missing data patterns including structured outages and sporadic gaps. The notebook provides visual insights and quantitative metrics to help determine the best approach for maintaining data integrity in downstream analytics. Success

Extended Kalman Filter imputation experiments for risk-factor time series - Braumeister-Stefan/Kalmans