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Learning short multivariate time series models through evolutionary and sparse matrix computation
Multivariate time series (MTS) data are widely available in different fields including medicine, finance, bioinformatics, science and engineering. Modelling MTS data accurately is important for many decision making activities. One area that has been largely overlooked so far is the particular type of time series where the data set consists of a large number of variables but with a small number of observations. In this paper we describe the development of a novel computational method based on Natural Computation and sparse matrices that bypasses the size restrictions of traditional statistical MTS methods, makes no distribution assumptions, and also locates the associated parameters. Extensive results are presented, where the proposed method is compared with both traditional statistical and heuristic search techniques and evaluated on a number of criteria. The results have implications for a wide range of applications involving the learning of short MTS models
The shape of the urine stream — from biophysics to diagnostics
We develop a new computational model of capillary-waves in free-jet flows, and apply this to the problem of urological diagnosis in this first ever study of the biophysics behind the characteristic shape of the urine stream as it exits the urethral meatus. The computational fluid dynamics model is used to determine the shape of a liquid jet issuing from a non-axisymmetric orifice as it deforms under the action of surface tension. The computational results are verified with experimental modelling of the urine stream. We find that the shape of the stream can be used as an indicator of both the flow rate and orifice geometry. We performed volunteer trials which showed these fundamental correlations are also observed in vivo for male healthy volunteers and patients undergoing treatment for low flow rate. For healthy volunteers, self estimation of the flow shape provided an accurate estimation of peak flow rate (+-2%). However for the patients, the relationship between shape and flow rate suggested poor meatal opening during voiding. The results show that self measurement of the shape of the urine stream can be a useful diagnostic tool for medical practitioners since it provides a non-invasive method of measuring urine flow rate and urethral dilation
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Predicting glaucomatous visual field deterioration through short multivariate time series modelling
In bio-medical domains there are many
applications involving the modelling of
multivariate time series (MTS) data. One area
that has been largely overlooked so far is the
particular type of time series where the data set
consists of a large number of variables but with
a small number of observations. In this paper we
describe the development of a novel computational
method based on genetic algorithms that bypasses
the size restrictions of traditional statistical
MTS methods, makes no distribution assumptions,
and also locates the order and associated
parameters as a whole step. We apply this method to the prediction and modelling of glaucomatous
visual field deterioration
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