UC San Diego

With the recent growth in data owing to ubiquitous internet connectivity, practical problems involving prediction which abound in multiple fields such as healthcare, finance, climate analysis, image processing and other disciplines are now becoming solvable. Traditionally such problems have been addressed by Model-Based approaches; i.e. by employing assumptions about the data generating process (DGP) to construct a model which can then used for predicting future values. However it is well known that such methods may suffer from disadvantages such as model non-robustness, overfitting among others. In this thesis along with Model-Based approaches the alternative paradigm of Model-Free Prediction is explored for problems such as regression, time series and random fields. The Model-Free framework does not assume knowledge of the DGP and is applied directly to the available data to construct estimators for both point prediction and prediction intervals. Both Model-Based and Model-Free prediction methods are applied to synthetic and real-life data

and their prediction performances are compared using standard metrics to demonstrate the applicability and usefulness of both approaches.