DEEP LEARNING-BASED BITCOIN BLOCK DATA PREDICTION SYSTEM TAKING INTO ACCOUNT TIME SERIES DISTRIBUTION CHARACTERISTICS

Disclosed are a deep learning-based Bitcoin block data prediction system and method that take into account time series distribution characteristics. The deep learning-based Bitcoin block data prediction system that takes into account time series distribution characteristics according to the present invention comprises: a data collection module for collecting a plurality of class data including block data, social media data, and price data; a pre-processing module which performs pre-processing for unifying the data formats of the plurality of collected class data as time series data, and clusters the time series data into time series data sets according to the distribution characteristics of the respective time series data for the plurality of pre-processed class data; a learning module which is trained on the plurality of clustered time series data sets through a deep learning-based model, and generates a plurality of prediction models according to the plurality of time series data sets; and a prediction module which evaluates the plurality of learned prediction models, and which, when new data is input, selects a prediction model with which to perform prediction from among the plurality of prediction models.