Bitcoin Price Estimation using Multiple Linear Regression and Neural Networks
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Abstract
Various studies have focused on estimating the price of cryptocurrencies using time series models and static variables. This study focuses on Bitcoin price prediction, using a model that combines multiple linear regression and neural networks. This approach makes it possible to identify the factors that influence Bitcoin volatility and, through a dynamic selection of variables, to constantly detect the most relevant set of characteristics for prediction. Likewise, the amount of data is optimized to improve precision and avoid overuse of historical information. The combination of these techniques captures underlying patterns and trends, increasing the reliability of predictions, with an accuracy of 88%. However, it is crucial to consider the need for continuous evaluations to adapt to changing market conditions. This approach provides a more accurate tool for making informed decisions in a highly volatile market.
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