A information scientist at Republic of india's prestigious Vellore Institute of Technology has outlined a method for how to purportedly predict crypto prices in existent-time using a Long Short-Term Memory (LSTM) neural network.

In a blog post published on Dec. 2, researcher Abinhav Sagar demonstrated a 4-step process for how to apply auto learning engineering to forecast prices in a sector he purported is "relatively unpredictable" equally compared with traditional markets.

Car learning for crypto cost 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. In back up of this claim, he argued that cryptocurrency prices fluctuate in accord with fast-paced technological developments, as well as economic, security and political factors.

Sagar's four-footstep 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.

Every bit software developer Aditi Mittal has outlined, LSTM is an acronym for "Long Short-Term Retentiveness" — a type of neural network that is designed to classify, procedure and predict time serial given time lags of unknown elapsing.

To train his network, Sagar used a dataset from CryptoCompare, making use of features such as price, volume and open, high and low values.

He provides a link to the code for the complete projection on GitHub and outlines the functions he used to normalize information values in preparation for auto learning.

Earlier plotting and visualizing the results of the network'due south predictions, Sagar notes he used Mean Absolute Error as an evaluation metric, which, he notes, measures the average magnitude of the errors in a set up of predictions, without considering their direction.

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

Sagar'southward visualization of his cryptocurrency predictions in real-time using an LSTM neural network. Source: towardsdatascience.com

From the markets to outer space

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

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

The bureau — whose chief part is the construction and operation of planetary robotic spacecraft and conducting Earth-orbit missions — further required qualifications in one or more related fields including motorcar learning, big data, Internet of Things, analytics, statistics and cloud computing.