Predictive Analytics

The integration of Predictive Analytics technology into WallitIQ was driven by its potential to deliver significant benefits, such as enhanced market forecasting, personalized investment strategies, and improved risk management. By leveraging AI and machine learning models to analyze historical data and identify trends, Predictive Analytics enables users to anticipate future market movements. This empowers them to make more informed investment decisions, tailored to expected market conditions.

Predictive analytics can analyze vast amounts of historical and real-time market data to identify trends and forecast future price movements. By examining factors such as trading volume, market sentiment, and macroeconomic indicators, AI algorithms can provide users with insights into potential market shifts. This capability allows users to make informed decisions about buying, selling, or holding their assets.

Here are some case studies that explain how machine learning models are highly effective in financial markets for predictive analytics. We intend to use these models in our wallet technology to make predictive analytics highly effective and practical.

Research	Reference
Prediction of Bitcoin prices with machine learning methods using time series data	(Karasu, 2018)
Prediction of cryptocurrency price dynamics with multiple machine-learning techniques	(Zhengyang, 2019)
An approach to predict and forecast the price of constituents and index of cryptocurrency using machine learning	(Chowdhury, 2022)
Predicting cryptocurrency prices using machine learning and deep learning techniques	(Vaddi, 2022)
Bitcoin price prediction using machine learning: An approach to sample dimension engineering	Chen, 2020
Cryptocurrency price prediction using traditional statistical and machine‐learning techniques: A survey	(Khedr, 2021)
Predicting the price of Bitcoin using machine learning	(McNally, 2018)
Predictions of Bitcoin prices through machine learning-based frameworks	(Cocco, 2021)