Data Scientist Uses Deep Learning to Predict BTC Price in Real-Time

A data scientist at India's prestigious Vellore Institute of Technology has outlined a method for how to purportedly predict crypto prices in real-time using a Long Short-Term Memory neural network.

In a blog post published on Dec. 2, researcher Abinhav Sagar demonstrated a four-step process for how to use machine learning technology to forecast prices in a sector he purported is "Relatively unpredictable" as compared with traditional markets.

Machine learning for crypto price prediction has been "Restricted".

Sagar prefaced his demonstration by noting that while machine learning has achieved some success in predicting stock market prices, its application in the cryptocurrency field has been restricted.

Sagar's four-step proposed method involves 1) collecting real-time cryptocurrency data; 2) preparing the data for neural network training; 3) testing the prediction using the LSTM neural network; 4) visualizing the results of the prediction.

As software developer Aditi Mittal has outlined, LSTM is an acronym for "Long Short-Term Memory" - a type of neural network that is designed to classify, process and predict time series given time lags of unknown duration.

He provides a link to the code for the complete project on GitHub and outlines the functions he used to normalize data values in preparation for machine learning.

Sagar's visualization of his cryptocurrency predictions in real-time using an LSTM neural network.

Beyond market predictions, the convergence of new decentralized technologies such as blockchain with machine learning has been gaining ever more traction.

As reported this fall, NASA recently published a listing for a data scientist role, singling out cryptocurrency and blockchain expertise as "a plus."