Deep convolutional autoencoder for cryptocurrency market analysis

2019/10/27 by Vladimir Puzyrev, Puzyrev, Vladimir
Computer Science · Decision Sciences · Economics, Econometrics and Finance · #Blockchain Technology Applications and Security #Complex Systems and Time Series Analysis #FOS: Computer and information sciences #FOS: Economics and business #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Statistical Finance (q-fin.ST) #Stock Market Forecasting Methods

paper · pdf · doi:10.48550/arxiv.1910.12281

openalex publication_date 2019/10/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• On a Test Whether Two Samples are from the Same Population
• The inefficiency of Bitcoin
• Conditional Time Series Forecasting with Convolutional Neural Networks
• Deep Clustering via Joint Convolutional Autoencoder Embedding and Relative Entropy Minimization

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