  Time series are series of values ordered by time. This kind of data can be
found in many real world settings. Classifying time series is a difficult task
and an active area of research. This paper investigates the use of transfer
learning in Deep Neural Networks and a 2D representation of time series known
as Recurrence Plots. In order to utilize the research done in the area of image
classification, where Deep Neural Networks have achieved very good results, we
use a Residual Neural Networks architecture known as ResNet. As preprocessing
of time series is a major part of every time series classification pipeline,
the method proposed simplifies this step and requires only few parameters. For
the first time we propose a method for multi time series classification:
Training a single network to classify all datasets in the archive with one
network. We are among the first to evaluate the method on the latest 2018
release of the UCR archive, a well established time series classification
benchmarking dataset.
