MLP-based Learnable Window Size Dataset for Bitcoin Market Price

The dataset of this paper is collected based on Google, Blockchain, and the Bitcoin market. Generally, there is a total of 26 features, however, a feature whose correlation rate is lower than 0.3 between the variations of price and the variations of feature has been eliminated. Hence, a total of 21 practical features including Market capitalization, Trade-volume, Transaction-fees USD, Average confirmation time, Difficulty, High price, Low price, Total hash rate, Block-size, Miners-revenue, N-transactions-total, Google searches, Open price, N-payments-per Block, Total circulating Bitcoin, Cost-per-transaction percent, Fees-USD-per transaction, N-unique-addresses, N-transactions-per block, and Output-volume have been selected. In addition to the values of these features, for each feature, a new one is created that includes the difference between the previous day and the day before the previous day as a supportive feature. From the point of view of the number and history of the dataset used, a total of 1275 training data were used in the proposed model to extract patterns of Bitcoin price and they were collected from 12 Nov 2018 to 4 Jun 2021.