Enhancing Bitcoin Price Predictions: A Q-Learning Approach to Mitigate Volatility and Uncertainty in Twitter Opinion-Based Forecasts

Abstract

There is a great deal of room for inaccuracy, speculation, fast price fluctuations, and ambiguity when tweeting about the Bitcoin price. Using the Q learning method to get accurate Bitcoin price projections is not feasible because to the intrinsic volatility and unpredictability of Twitter views. Bitcoin price volatility, the likelihood of errors, and the difficulties in generating accurate results with the present Q learning strategy all contribute to the fact that there is no certain method to predict Bitcoin values using Twitter views. The suggested Q-learning approach is an effort to address these concerns; it places a higher value on enhanced accuracy, precision, and flexibility than on lowering volatility and uncertainty in Twitter-based prediction models. One of the major challenges in achieving reliable Bitcoin price predictions is training Q-learning to handle the dynamic nature of sentiment data from Twitter. With Bitcoin price estimates based on Twitter perspectives inherently unstable and prone to speculation, the suggested Q-learning technique aims to overcome these shortcomings. As an example, it is difficult to draw accurate inferences from Twitter sentiment data because of the wide range of opinions expressed within, as this data is always changing. The suggested Q learning method for Bitcoin price forecasting using Twitter views surpasses the current system in terms of accuracy, precision, and flexibility by mitigating the system's intrinsic volatility and uncertainty while maintaining a competitive degree of temporal complexity.