  This study attempts to analyze patterns in cryptocurrency markets using a
special type of deep neural networks, namely a convolutional autoencoder. The
method extracts the dominant features of market behavior and classifies the 40
studied cryptocurrencies into several classes for twelve 6-month periods
starting from 15th May 2013. Transitions from one class to another with time
are related to the maturement of cryptocurrencies. In speculative
cryptocurrency markets, these findings have potential implications for
investment and trading strategies.
