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.

Figure1: MIT researchers found new software for data prediction

Figure 1 shows to make these powerful tools more user-friendly; MIT researchers developed a system that directly integrates prediction functionality on top of an existing time-series database.

Their simplified interface, which they call tspDB (time series predict database), does all the complex modeling behind the scenes so a nonexpert can easily generate a prediction in only a few seconds.

The new system is more accurate and more efficient than state-of-the-art deep learning methods when performing two tasks: predicting future values and filling in missing data points. [1]

One reason tspDB is so successful is that it incorporates a novel time-series-prediction algorithm, explains electrical engineering and computer science (EECS) graduate student Abdullah Alomar, an author of a recent research paper in which he and his co-authors.

This algorithm is especially effective at making predictions on multivariate time-series data, which are data that have more than one time-dependent variable. [2]

Researchers have been working for years on the problem of interpreting time series data, adapting various algorithms and integrating them into tspDB as they built the interface.

About four years ago, they learned about a particularly powerful classical algorithm called singular spectrum analysis (SSA), which allocates and predicts single time series.

The single time series algorithm transformed it into a matrix and used matrix estimation procedures. “stack” the matrices for each individual time series, treat it as one big matrix, and apply the single time series algorithm to it.

This naturally uses information across multiple time series, both across the time series and across time, that they describe in their new paper.

“The variant of mSSA we introduced sums it all up beautifully, so it provides not only the most probable estimate, but also a time-varying confidence interval,” author says.

Their algorithm outperformed all the others on imputation and it outperformed all but one of the other algorithms when it came to predicting future values. The researchers also showed that their modified version of mSSA can be applied to all types of time series data. [3]

References:

1. https://techxplore.com/news/2022-03-interface-time-series.html
2. https://scitechdaily.com/mit-researchers-create-a-tool-for-predicting-the-future/
3. source: https://badpi.com/simplified-interface-for-time-series-data-predictions/

Cite this article:

Sri Vasagi K (2022), A new software for time series data prediction, Anatechmaz, pp. 37