  Time series data mining is an important field of research in the era of "Big
Data". Next generation astronomical surveys will generate data at unprecedented
rates, creating the need for automated methods of data analysis. We propose a
method of light curve characterisation that employs a pipeline consisting of a
neural network with a Long-Short Term Memory Variational Autoencoder
architecture and a Gaussian mixture model. The pipeline performs extraction and
aggregation of features from light curve segments into feature vectors of fixed
length which we refer to as light curve "fingerprints". This representation can
be readily used as input of down-stream machine learning algorithms. We
demonstrate the proposed method on a data set of Rossi X-ray Timing Explorer
observations of the galactic black hole X-ray binary GRS 1915+105, which was
chosen because of its observed complex X-ray variability. We find that the
proposed method can generate a representation that characterises the
observations and reflects the presence of distinct classes of GRS 1915+105
X-ray flux variability. We find that this representation can be used to perform
efficient classification of light curves. We also present how the
representation can be used to quantify the similarity of different light
curves, highlighting the problem of the popular classification system of GRS
1915+105 observations, which does not account for intermediate class behaviour.
