Making predictions using time-series data typically requires several data-processing steps and the use of complex machine learning algorithms, which have such a steep learning curve they aren’t readily accessible to nonexperts. This is where deep learning technology is usually applied, but maybe there is a different option. This interesting information came to us from MIT News in their article, “A tool for predicting the future.”

MIT researchers developed a system that directly integrates prediction functionality on top of an existing time-series database. Their simplified interface does all the complex modeling behind the scenes so a nonexpert can easily generate a prediction in only a few seconds.

This new approach incorporates a novel time-series-prediction algorithm which is exceptionally good at predicting future values and filling in missing data points.

It makes you wonder in what other applications this could be useful. But also it is important to remember how important it is to understand the technology in addition to being impressed with it. Explainable AI allows users to comprehend and trust the results created by machine learning algorithms. “Explainable AI” is used to describe an AI model, its expected impact and potential biases. Why is this important? Because the results can have an impact on data security or safety.

Melody K. Smith

Data Harmony is an award-winning semantic suite that leverages explainable AI.

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