  We present a novel approach for time series classification where we represent
time series data as plot images and feed them to a simple CNN, outperforming
several state-of-the-art methods. We propose a simple and highly replicable way
of plotting the time series, and feed these images as input to a non-optimized
shallow CNN, without any normalization or residual connections. These
representations are no more than default line plots using the time series data,
where the only pre-processing applied is to reduce the number of white pixels
in the image. We compare our method with different state-of-the-art methods
specialized in time series classification on two real-world non public
datasets, as well as 98 datasets of the UCR dataset collection. The results
show that our approach is very promising, achieving the best results on both
real-world datasets and matching / beating the best state-of-the-art methods in
six UCR datasets. We argue that, if a simple naive design like ours can obtain
such good results, it is worth further exploring the capabilities of using
image representation of time series data, along with more powerful CNNs, for
classification and other related tasks.
