Time Series Analysis with a Skewed Kalman Filter
The analysis of time series has always played a central role in statistics. In this chapter, we focus on temporal state-space models for time series that can be decomposed in a sum of three components: a smooth trend, a skewness part, and noise. The ﬁrst one is supposed to be deterministic and the other two random. Such time series decompositions are common in paleoclimate studies. The central element of the estimation procedure is the Kalman ﬁlter that has to be modiﬁed to deal with skewness.