Singular spectrum analysis (SSA) is a method of time-series analysis based on the singular value decomposition of an associated Hankel matrix. We present an approach to SSA using an effective and numerically stable high-degree polynomial approximation of a spectral projector, which also provides a means of time-series forecasting. Several numerical examples illustrating the algorithm are given.

In this article, we describe an approximate dynamic programming (ADP) approach to compute lower bounds on the optimal value function for a discrete time, continuous space, and infinite horizon setting. The iteratively constructs family of bounding functions by using so-called Bellman inequality. novelty our is that, at each iteration, aim that maximizes point-wise maximum taken with computed th...