masterThesis
A methodology for detection of causal relationships between discrete time series on systems
Fecha
2019-01-25Registro en:
ABREU, Rute Souza de. A methodology for detection of causal relationships between discrete time series on systems. 2019. 65f. Dissertação (Mestrado em Engenharia Elétrica e de Computação) - Centro de Tecnologia, Universidade Federal do Rio Grande do Norte, Natal, 2019.
Autor
Abreu, Rute Souza de
Resumen
The need for detecting causality relations of process, events or variables is present
in many areas of knowledge, e.g., distributed computing, the stock market, industry and
medical sector. This occurs because the knowledge of these relations can often be helpful
in solving a variety of problems. For example, maintaining the consistency of replicated
databases when writing distributed algorithms or optimizing the purchase and sale of
stocks in the stock market. In this context, this dissertation proposes a new methodology
for detecting causality relations in systems by using information criteria and Bayesian
networks to generate the most probable structure of connections between discrete time
series. Modeling the system as a directed graph, in which the nodes are the discrete
time series and the edges represent the relations, the main idea of this work is to detect
causality relations between the nodes. This detection is made using the method of transfer
entropy, which is a method to quantify the information transferred between two variables,
and the K2 algorithm: a heuristic method whose objective is to find the most probable
belief-network structure, given a data set. Because K2 depends on the premise of having
a previous structure that defines the hierarchy among the network nodes, it is proposed in
the methodology the creation of the previous ordering on the nodes considering direct and
indirect relations, and the modeling of these relations according to the lag between cause
and effect. In addition, knowing that the K2 algorithm considers that each case of the data
set occurs simultaneously, the proposed methodology modifies the original algorithm by
inserting the dynamics of these lags into it. This modification provides a mechanism for
comparing direct and indirect causality relations regarding its contribution to the structure.
As the result, it is obtained a graph of causality relations between the series, with the
relation’s lags being explicit.