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Machine Learning for Structure Discovery in Vector Autoregressive Processes

Show simple item record 2015-10-06T08:31:40Z und 2017-10-24T12:21:47Z 2015-10-06T08:31:40Z und 2017-10-24T12:21:47Z 2015-10-06T08:31:40Z
dc.identifier.uri und
dc.title Machine Learning for Structure Discovery in Vector Autoregressive Processes en
ethesis.discipline Applied Mathematics en
ethesis.discipline Soveltava matematiikka fi
ethesis.discipline Tillämpad matematik sv
ethesis.department Institutionen för matematik och statistik sv
ethesis.department Department of Mathematics and Statistics en
ethesis.department Matematiikan ja tilastotieteen laitos fi
ethesis.faculty Matematisk-naturvetenskapliga fakulteten sv
ethesis.faculty Matemaattis-luonnontieteellinen tiedekunta fi
ethesis.faculty Faculty of Science en
ethesis.faculty.URI Helsingfors universitet sv University of Helsinki en Helsingin yliopisto fi
dct.creator Suotsalo, Kimmo
dct.issued 2015
dct.language.ISO639-2 eng
dct.abstract Vector or multivariate autoregression is a statistical model for random processes. It is relatively simple yet flexible enough to describe many real-world phenomena. Stochastic processes modelled by multivariate autoregression are called vector autoregressive (VAR) processes. The structure of a VAR process is determined by the conditional independences of the variables and the lag length that describes the duration of direct influence. Structure discovery in VAR processes refers to finding reasonable candidates for these elements. Learning the structure of a VAR process can be realized using graphical models, where nodes represent variables and edges represent absence of conditional independence. This transforms the problem of learning conditional independences of variables into the problem of finding edges between nodes. This thesis extends previous studies to make inference on the structure of VAR processes involving tens or hundreds of variables, without assuming the underlying Granger causality graphs to be decomposable. A scoring function capable of predicting the Markov blankets of the nodes is derived and proved to be consistent. This scoring function is combined with another scoring function to discover VAR structures from multivariate time series. The performance of the proposed method is tested on synthetic data. In all test cases that are considered, given enough samples and some a priori information, the true lag length can be identified, the true positive rate made higher than 0.94, and the false positive rate kept below 0.01. en
dct.language en
ethesis.language English en
ethesis.language englanti fi
ethesis.language engelska sv
ethesis.thesistype pro gradu-avhandlingar sv
ethesis.thesistype pro gradu -tutkielmat fi
ethesis.thesistype master's thesis en
dct.identifier.urn URN:NBN:fi-fe2017112251654
dc.type.dcmitype Text

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