Deep convolutional autoencoder for cryptocurrency market analysis
This work addresses pattern recognition for cryptocurrency traders and investors, but it is incremental as it applies an existing method to a new domain.
The study tackled pattern analysis in cryptocurrency markets using a convolutional autoencoder to classify 40 cryptocurrencies over twelve 6-month periods from May 2013, finding that class transitions relate to cryptocurrency maturation, with potential implications for investment strategies.
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.