WiMi Developed Efficient Prediction Models for Cryptocurrency Markets Based on Machine Learning

Core Viewpoint - WiMi Hologram Cloud Inc. has developed a hybrid LSTM-ELM model that utilizes multi-scale analysis and machine learning techniques to enhance cryptocurrency price prediction accuracy, addressing the complexities of the digital currency market [1][6][7] Group 1: Model Development - The hybrid LSTM-ELM model integrates advanced methods such as multi-scale analysis, artificial intelligence, and signal decomposition to improve prediction accuracy [2][5] - The model begins with thorough data preparation, including handling missing data, outlier detection, and data normalization, which are crucial for accurate predictions [2][3] - The model decomposes cryptocurrency price time series into high, medium, and low-frequency components to better capture price fluctuations [3][4] Group 2: Methodology - The sample entropy method is employed to measure the similarity of time series, allowing for a more accurate reconstruction of frequency components [3][4] - Empirical Modal Decomposition (EMD) and Variational Modal Decomposition (VMD) are used to enhance the decomposition of nonlinear and unstable data, improving prediction capabilities [4][6] - The model combines predictions from different frequency components, leveraging deep learning algorithms like LSTM and ELM to adapt to market variations [5][6] Group 3: Market Context and Implications - The hybrid LSTM-ELM model represents a significant innovation in financial technology, particularly in the context of the booming digital currency market [6][7] - The model's ability to handle both high and low-frequency components makes it a powerful tool for investors navigating market volatility [6][7] - WiMi's advancements in predictive modeling not only aim to provide investors with accurate market information but also signal future developments in the financial technology industry [7]