Structured Output Regression for Time Series Prediction

In this talk, I will introduce a novel approach to robust time series prediction, which is formulated as a structured output regression problem by simultaneously considering the prediction error and the shape of the predicted sequence in model training. I also show that a simplified version of the generic model employing linear regression and PCA leads to a single optimization problem with convex objective function and globally optimal solution.