Signal Processing on Graphs: Causal Modeling of Unstructured Data

Understand

Many applications collect a large number of time series, for example, the financial data of companies quoted in a stock exchange, the health care data of all patients that visit the emergency room of a hospital, or the temperature sequences continuously measured by weather stations across the US.

• These data are often referred to as unstructured.
• A first task in its analytics is to derive a low dimensional representation, a graph or discrete manifold, that describes well the interrelations among the time series and their intrarelations across time.
• This paper presents a computationally tractable algorithm for estimating this graph that structures the data.