BEIJING, Feb. 27,
2024 /PRNewswire/ -- 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.
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
This press release contains "forward-looking statements" within
the Private Securities Litigation Reform Act of 1995. These
forward-looking statements can be identified by terminology such as
"will," "expects," "anticipates," "future," "intends," "plans,"
"believes," "estimates," and similar statements. Statements that
are not historical facts, including statements about the Company's
beliefs and expectations, are forward-looking statements. Among
other things, the business outlook and quotations from management
in this press release and the Company's strategic and operational
plans contain forward−looking statements. The Company may also make
written or oral forward−looking statements in its periodic reports
to the US Securities and Exchange Commission ("SEC") on Forms 20−F
and 6−K, in its annual report to shareholders, in press releases,
and other written materials, and in oral statements made by its
officers, directors or employees to third parties. Forward-looking
statements involve inherent risks and uncertainties. Several
factors could cause actual results to differ materially from those
contained in any forward−looking statement, including but not
limited to the following: the Company's goals and strategies; the
Company's future business development, financial condition, and
results of operations; the expected growth of the AR holographic
industry; and the Company's expectations regarding demand for and
market acceptance of its products and services.
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.
View original
content:https://www.prnewswire.com/news-releases/wimi-developed-efficient-prediction-models-for-cryptocurrency-markets-based-on-machine-learning-302072518.html
SOURCE WiMi Hologram Cloud Inc.