MIT researchers develop new tool to predict the future using dataLeigh Mc Gowranon April 15, 2022 at 14:36 Silicon RepublicSilicon Republic


Researchers at MIT claim to have made a tool that helps nonexperts in various fields make predictive forecasts using collected data.

The research team explained that making predictions using time-series data usually requires several data-processing steps and complex machine-learning algorithms, which have steep learning curves.

This can make using data difficult for various areas that benefit from these predictions, such as weather forecasts, estimating future stock prices or looking at a patient’s risk of developing a disease.

To make predicting future outcomes easier, the research team made a system that integrates prediction functionality on top of an existing time-series database.

The team said its new interface – called tspDB – does the complex modelling behind the scenes so nonexperts can easily generate predictions in seconds.

“Even as the time-series data becomes more and more complex, this algorithm can effectively capture any time-series structure out there,” Senior author of the study Devavrat Shah said in an accompanying article. “It feels like we have found the right lens to look at the model complexity of time-series data.”

The researchers said a powerful algorithm at the heart of their tool can transform multiple time series datasets into a tensor, which is a multi-dimensional array of numbers.

The algorithm is very effective at analysing data that has more than one time-dependent variable. For example, elements of a weather database such as temperature, dew point and cloud cover each depend on their past values.

The team said their model is more accurate and efficient than state-of-the-art deep learning methods when predicting future values and filling in missing data points.

“One reason I think this works so well is that the model captures a lot of time series dynamics, but at the end of the day, it is still a simple model,” study author Abdullah Alomar said. “When you are working with something simple like this, instead of a neural network that can easily overfit the data, you can actually perform better.”

Shah and his collaborators have been working on the problem of interpreting time-series data for years. Now that they’ve had success, their next goal is to make the algorithm accessible to everyone.

The researchers are targeting new algorithms that can be incorporated into tspDB and plan to gather more feedback from current users to see how they can improve the system’s functionality and user-friendliness.

“Our interest at the highest level is to make tspDB a success in the form of a broadly utilisable, open-source system,” Shah said. “Time-series data are very important, and this is a beautiful concept of actually building prediction functionalities directly into the database.”

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