Researchers from IIIT Delhi used the conventional Baum-Welch algorithm and deep learning to devise novel AI-driven approaches to predict crypto pricing and other financial parameters. As cryptocurrencies are not pegged against standard parameters or products, speculating their prices is challenging.
Shalini Sharma, a Ph.D. scholar from IIIT-Delhi, and her supervisor Dr. Angshul Majumdar devised two predominant approaches to predict financial parameters like crypto prices. Elsevier Information Sciences has recently validated the work and declared it to be “very precise” in predicting future prices.
The first approach builds on the Baum-Welch framework, a particular case of expectation-maximization used in a Hidden Markov Model (HMM). Using this framework, users can predict not only the prices but also the prediction uncertainty. This strategy calls for understanding the underlying factors influencing price swings, which is only sometimes possible with cryptocurrency.
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The research also shows that the uncertainty estimates received from the first approach are correlated with historical CVI values. CVI, or crypto volatility index, shows how crypto prices react to fluctuations over time.
The second approach is driven by DL, as it does not require prior knowledge about underlying factors. This approach can predict crypto prices but cannot give uncertainty, making it ineffective in interpretability aspects.
The work is a significant step forward to aid crypto enthusiasts in having confidence in mining cryptocurrencies.
