Clustering discrete-valued time series

Clustering life trajectories: A new divisive hierarchical clustering algorithm for discrete-valued discrete time series

Smoothing Estimation for Discrete-Valued Time Series

We deal with the smoothed estimators for conditional probability functions of discrete-valued time series fY t g under two diierent settings. When the conditional distribution of Y t given its lagged values falls in a parametric family and depends on exogenous random variables, a smoothed maximum (partial) likelihood estimator for the unknown parameter is proposed. While there is no prior infor...

Time-Series Data Clustering

15.

Subsequence Time Series Clustering

INTRODUCTION Clustering analysis is a tool used widely in the Data Mining community and beyond (Everitt et al. 2001). In essence, the method allows us to " summarise " the information in a large data set X by creating a very much smaller set C of representative points (called centroids) and a membership map relating each point in X to its representative in C. An obvious but special type of data...

Discrete-time, discrete-valued observable operator models: a tutorial

This tutorial gives a basic yet rigorous introduction to observable operator models (OOMs). OOMs are a recently discovered class of models of stochastic processes. They are mathematically simple in that they require only concepts from elementary linear algebra. The linear algebra nature gives rise to an efficient, consistent, unbiased, constructive learning procedure for estimating models from ...