$WiMi Hologram Cloud (WIMI.US)$ Cryptocurrency prices vary a...

Cryptocurrency prices vary across time, and it is difficult for a single model to fully capture these. A multi-scale analysis approach, matching different machine learning algorithms with corresponding multi-scale components to construct a more comprehensive cryptocurrency price prediction model.

The model begins with detailed data preparation and pre-processing of raw cryptocurrency price data. This includes steps such as processing of missing data, detection and repair of outliers, and data normalization. Ensuring the quality of the input data is critical to constructing an accurate predictive model.

Using the sample entropy method, the high, medium, and low-frequency sub-components obtained are decomposed according to the similarity and frequency pairs of the sub-components, and then combined. 

On the basis of the obtained high, medium and low-frequency components, the decomposition is further carried out using a combination of Empirical Modal Decomposition (EMD) and Variational Modal Decomposition (VMD). 

The hybrid LSTM-ELM model was constructed by combining the predictions of different frequency components. In this way, the model is able to more fully understand and predict the fluctuations in the price of the cryptocurrency Bitcoin.

Against the backdrop of the current booming digital currency market, the hybrid LSTM-ELM model marks an important innovation in the field of financial technology.

Deep learning algorithms enhanced data learning capabilities for the model, allowing it to better understand and adapt to the nonlinear characteristics of the cryptocurrency market. Supported by empirical results, the model has a superior prediction. The hybrid LSTM-ELM model points the way to the future development of the financial technology industry, which will bring new ideas and methods.