Recalculating Patterns in Real Time

Intriguing approach for constantly changing time series data.

Novel Algorithm Enables Statistical Analysis of Time Series Data 

MIT News  By Sara Cody

Researchers at the Massachusetts Institute of Technology (MIT) have developed state-space multitaper time-frequency analysis (SS-MT), a unique algorithm they say delivers time series dataset analysis in real time. The team notes SS-MT enables scientists to work in a more informed manner with large, nonstationary datasets so they can not only measure the fluid properties of data but also make formal statistical comparisons between arbitrary data segments. "The algorithm functions similarly to the way a [global-positioning system] calculates your route when driving," says MIT professor Emery Brown. The team tested SS-MT by first analyzing electroencephalogram readings from patients receiving general anesthesia for surgery. The program produced a de-noised spectrogram defining changes in power across frequencies over time, and the researchers also applied SS-MT's inference paradigm to compare different levels of unconsciousness in terms of the differences in the spectral properties of these behavioral states. "The SS-MT analysis produces cleaner, sharper spectrograms," Brown says.  .... "