Evaluation of Conditional Recurrent Generative Adversarial Networks forMultivariate Time-Series Augmentation
Typ
Examensarbete för masterexamen
Program
Engineering mathematics and computational science (MPENM), MSc
Publicerad
2020
Författare
Carlsson, Anna
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
A successful application of any machine learning algorithmis dependent on a sufficiently large
training dataset, preferably class-balanced and correctly labeled. However, in many applications,
the collection and labeling of data is time-consuming, expensive, and might require special
security precautions if the data is of a sensitive nature. Therefore, different types of augmentation
methods are commonly used. For time-series data, traditional augmentation methods such as
rotation, translation, and flipping are not applicable. In applications where the dataset consists
of time-series data, other augmentation methods are therefore of interest.
In this thesis, the usage of generative adversarial networks (GANs) as an augmentation method
for univariate and multivariate time-series data is investigated. Both recurrent and conditional
recurrent GANs are examined. Apart from constructing architectures for time-series generation,
the thesis focuses on finding suitable methods for evaluating the quality of the generated data.
To monitor the training progress and select a suitable generator model to simulate synthetic data
from, two distance-based kernel metrics are used: maximum mean discrepancy (MMD) and
energy distance (ED). To evaluate the sample quality and diversity of the generated data, several
experiments are performed where a classifier is trained on real, tested on synthetic data (TRTS),
trained on synthetic, tested on real data (TSTR), and lastly trained and tested on a mixture of real
and synthetic data (TMTM). Furthermore, experiments aiming to examine the usage of synthetic
samples from conditional recurrent GANs to augment a real dataset are performed.
The results indicate that the GANs successfully generates highly realistic samples, both of simpler
time-series and more complexmultivariate time-series. However, the time-series seem to not aid
a classifier to any large extent when added to real data, even when larger proportions of synthetic
data are added. A possible explanation for this is that the synthetic data, although consisting of
realistic samples, suffers from loss of in-class diversity and boundary distortion.
Beskrivning
Ämne/nyckelord
deep learning, generative adversarial networks, generative models,multivariate timeseries classification, maximummean discrepancy, energy distance, covariate shift, boundary distortion