Beijing, Jan. 22, 2024 (GLOBE NEWSWIRE) -- WiMi Hologram Cloud Inc. (NASDAQ: WIMI) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, today announced an LSTM-based data analysis system to provide clients with cutting-edge tools to trade in the complex cryptocurrency environment.
As a decentralized digital currency, the price of Bitcoin is affected by a variety of factors, such as market demands, policy regulations, and technological innovations. Therefore, the prediction of price trends needs to comprehensively consider these factors and find patterns from a large amount of data. Traditional data analysis methods make it difficult to handle such complex data, but the LSTM algorithm can solve this problem.
WiMi uses the LSTM algorithm (a machine learning algorithm) to predict cryptocurrency prices, which allows it to more accurately predict the price of Bitcoin. The LSTM algorithm is a recurrent neural network. The system uses a variety of data sources, including historical prices, transaction volumes, social media data, and more. The system uses the LSTM algorithm to analyze these data and generate predictions of bitcoin price trends. LSTM is a special type of RNN architecture that can efficiently handle time-series-dependent data. It avoids the problem of gradient vanishing or gradient explosion when dealing with long-term dependencies by introducing a "gate" structure to control the flow of information. This makes LSTM widely used in the fields of speech recognition, natural language processing, and time series analysis.
Cryptocurrency price is sequential, with each piece of data dependent on the one before it. The ability of LSTMs to process and memorize information over extended sequences allows them to capture complex patterns that traditional models might miss. The "long" in LSTM refers to the model's ability to retain information over a longer period. This is critical in the cryptocurrency market, and LSTM's long-term memory makes it adept at recognizing and exploiting these trends. Cryptocurrency markets are non-linear and dynamic, characterized by sudden and unpredictable changes. LSTM's ability to model non-linear relationships allows it to adapt to changing markets. LSTM is adept at automatically learning and extracting relevant features from input data. In the context of the predictable price of Bitcoin, this means that the model can identify and utilize important market metrics, thus simplifying the development process.
WiMi utilizes the LSTM algorithm to build an efficient data analysis system that is capable of deep learning from historical Bitcoin transaction data to extract key factors that influence price trends. The system mainly includes the following modules:
Data pre-processing: Processing raw data to ensure the quality of the data. This includes cleaning the data, dealing with missing values, and normalizing the data to ensure that the inputs to the algorithm are consistent and meaningful.
Model architecture: The architecture of the LSTM model is a critical component of its effectiveness. WiMi leveraged its expertise in deep learning to design a sophisticated architecture that balances model complexity, optimizing prediction accuracy and real-world applicability.
Hyper-parameter tuning: Fine-tuning the parameters of the LSTM model is critical to achieving optimal performance. using advanced optimization techniques, WiMi systematically explores the hyper-parameter space to ensure model robustness and adaptability to varying market conditions.
Training and validation: Training an LSTM model requires a large amount of data. WiMi carefully selects the data and divides it into training and validation sets to avoid over-fitting. Training the LSTM model with historical data allows it to learn and model the dynamics of the Bitcoin price.
Prediction and Evaluation: Based on the extracted features and the trained model, the bitcoin price is predicted, and the accuracy of the prediction is evaluated through cross-validation and other methods.
Real-time update and optimization: Based on the latest market data and feedback, the model is constantly updated and optimized to ensure the accuracy of the prediction.
Continuous learning: Recognizing the dynamic nature of the cryptocurrency market, WiMi has implemented a continuous learning system. This allows the LSTM model to adapt to changing markets, incorporating new data and enhancing its predictive capabilities.
WiMi's data analysis system benefits from the advanced LSTM algorithm, which not only has superior learning and memory capabilities but also uses deep learning to extract key factors affecting the price of Bitcoin from complex data, thus ensuring the high accuracy of the system's predictions. The system's real-time nature is also a compelling feature, enabling it to instantly process the latest market data and provide investors with rapidly generated price trend forecasts, enabling them to make sharp decisions in a rapidly changing market.
On the other hand, the system demonstrates excellent scalability, with the ability to flexibly expand in response to changes in data volume to meet data analysis of different sizes and needs. This flexibility allows the system to adapt to the diversity of markets and data distribution, thus maintaining high prediction accuracy under different environments. At the same time, the LSTM model can provide investors with more credible reasons and increase trust in decision-making compared to traditional black box models.
WiMi's LSTM-based Bitcoin price prediction data analysis system is important for cryptocurrency and other industries. Investors and traders can use accurate price predictions to make informed decisions and minimize the risks associated with market volatility. WiMi's system enables users to make strategic decisions using data-driven insights. The LSTM algorithm makes complex algorithmic trading strategies simple. Traders can automate buy and sell decisions based on the model's predictions, capitalizing on market opportunities in real time. Accurate price predictions help improve market efficiency by reducing information asymmetry. As more and more people adopt advanced predictive models, markets are likely to become more rational and less prone to irrational exuberance or panic selling.
The cryptocurrency market, and Bitcoin in particular, provides a dynamic and challenging environment for traders. By addressing the unique challenges of the cryptocurrency market and harnessing the power of LSTM, WiMi aims to revolutionize the way traders take advantage of the opportunities presented by the volatility of the Bitcoin price. As WiMi continues to break new ground in technological innovation, their fruition has even influenced predictive analysis and algorithmic trading.
About WIMI Hologram Cloud
WIMI Hologram Cloud, Inc. (NASDAQ:WIMI) is a holographic cloud comprehensive technical solution provider that focuses on professional areas including holographic AR automotive HUD software, 3D holographic pulse LiDAR, head-mounted light field holographic equipment, holographic semiconductor, holographic cloud software, holographic car navigation and others. Its services and holographic AR technologies include holographic AR automotive application, 3D holographic pulse LiDAR technology, holographic vision semiconductor technology, holographic software development, holographic AR advertising technology, holographic AR entertainment technology, holographic ARSDK payment, interactive holographic communication and other holographic AR technologies.
Safe Harbor Statements
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Further information regarding these and other risks is included in the Company's annual report on Form 20-F and the current report on Form 6-K and other documents filed with the SEC. All information provided in this press release is as of the date of this press release. The Company does not undertake any obligation to update any forward-looking statement except as required under applicable laws.
Contacts
WIMI Hologram Cloud Inc.
Email: pr@wimiar.com
TEL: 010-53384913
ICR, LLC
Robin Yang
Tel: +1 (646) 975-9495
Email: wimi@icrinc.com
