Neirbi collects, cleans, curates and appends massive amounts of data, and pushes it through a proprietary algorithm to deliver actionable insights and business outcome predictions in real-time.
For over two years, Neirbi has collected, cleaned and augmented massive amounts of relevant data, preparing it to be used by our algorithms to make predictions.
Neirbi collects real-time data including:
The data is cleaned, normalized and augmented to include ancillary information, which distinguishes the collection process from any other on the market. Finally, the data is formatted into collections so that it can be easily accessed by the Neirbi LSTM to make predictions.
Teaming up with award-winning data scientists who received their Ph.D’s from Yale University and specialize in statistical analysis, mathematics and deep neural networks, Neirbi is able to analyze all data sets, both independently and simultaneously, to identify real-time trends and predictions with the Neirbi LSTM engine.
Neirbi’s “missing value propagation” method addresses the ability to account for missing values in data sets that are fed through the LSTM model. This method allows Neirbi to leverage incomplete streams of data to contribute to better predictions, all the while maintaining high accuracy in outputs.