WiMi Hologram Cloud Says Co Developed Efficient Prediction Models For Cryptocurrency Markets Based On Machine Learning

Author: Benzinga Newsdesk | February 27, 2024 09:08am

WiMi Hologram Cloud Inc. (NASDAQ:WIMI) ("WiMi" or the "Company") is a leading global Hologram Augmented Reality ("AR") Technology provider. Based on machine learning, deep learning and other techniques, it focuses on developing efficient forecasting models applicable to the cryptocurrency market. Cryptocurrency prices vary across time, and it is difficult for a single model to fully capture these. Therefore, WiMi chose a multi-scale analysis approach, matching different machine learning algorithms with corresponding multi-scale components to construct a more comprehensive cryptocurrency price prediction model.

WiMi put its emphasis on the hybrid LSTM-ELM model that combines advanced methods such as multi-scale analysis, artificial intelligence, and signal decomposition. 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. Decompose the time series of raw cryptocurrency prices into different frequency components. The goal is to isolate high, medium, and low frequencies to better understand and capture price fluctuations.

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. The sample entropy method is a method used to measure the similarity of the time series, which takes into account the interrelationships and frequency features of the sub-components, thus better describing the overall structure of the time series. According to the results of the sample entropy method, the high, medium and low-frequency components are reconstructed separately. This step is to recombine the combined sub-components to get the high, medium and low-frequency components that are more accurate to the original cryptocurrency price.

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). Both EMD and VMD are classical methods for signal decomposition. By this, the decomposition effect for nonlinear and unstable data is improved. Prediction is performed using suitable algorithms for high and low-frequency components respectively. Deep learning algorithms such as LSTM and Extreme Learning Machines (ELM) may be more suitable for high and low-frequency components as they are better able to handle complex modes in these frequency ranges.

The hybrid LSTM-ELM model was constructed by combining the predictions of different frequency components. This aims to combine the information from each frequency component to improve the overall prediction accuracy of the model. In this way, the model is able to more fully understand and predict the fluctuations in the price of the cryptocurrency Bitcoin.

WiMi's hybrid LSTM-ELM model by choosing different machine learning algorithms, such as LSTM and ELM, the model better adapts to market variations in different frequency ranges and improves prediction accuracy. This means that the model is able to maintain better predictive performance under different market conditions, making it a reliable tool for investors.

Against the backdrop of the current booming digital currency market, WiMi's hybrid LSTM-ELM model marks an important innovation in the field of financial technology. Through multi-scale analysis, signal decomposition, intelligent matching of machine learning algorithms, and optimization of integration methods, the model successfully addresses the complexity and diversity of cryptocurrency market forecasting. Its powerful non-linear modeling capabilities, and adaptability to both high and low frequency components, make the model a powerful tool for investors in the face of market volatility.

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. WiMi's hybrid LSTM-ELM model not only promises to provide investors with more comprehensive and accurate market information, but also points the way to the future development of the financial technology industry, which will bring new ideas and methods.

Posted In: WIMI