MIT researchers created a simple tool that enables people to make highly accurate predictions
using a user-friendly interface that helps non-experts make effective forecasts using data collected over time.
A time series is a sequence taken at successive spaced points in time. Thus it is a sequence of discrete-time data.
Time series prediction comprises methods for analyzing time-series data to extract meaningful statistics and other data characteristics.
Time series forecasting uses a specific model to predict future values based on previously observed values.
Whether someone is trying to predict tomorrow's weather, forecast future stock prices, identify missed opportunities for sales
or even estimate your YouTube analytical trends, they will likely need to interpret time-series data.
Making predictions using time-series data typically requires several data-processing steps and complex machine-learning algorithms.
They have such a steep learning curve that they aren't readily accessible to non-experts.
To make these powerful tools more user-friendly, MIT researchers developed a system
that directly integrates prediction functionality with 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 non-expert can quickly generate a prediction in only a few seconds.
There are many kinds of methods or systems for time series database predictions.
This new system is more accurate and efficient than state-of-the-art deep learning methods
when performing two tasks: predicting future values and filling in missing data points.
The algorithm is so successful because it incorporates a novel time-series prediction strategy.
This algorithm is especially effective at making predictions on multivariate time-series data,
which are data that have more than one time-dependent variable.
For instance, temperature, cloud cover in a weather database depend on past values.
The algorithm also estimates the volatility of a multivariate time series to provide the user with a confidence level for its predictions.
Even as the time-series data becomes more and more complex, this algorithm can effectively capture any time-series structure.
The algorithm is a matrix and utilizes matrix estimation procedures.
The critical intellectual challenge was how to adapt it to use multiple time series.
After a few years of study, researchers have found a straightforward way:
"Stack" the matrices for each time series, treat it as one big matrix and then apply the single time-series algorithm.
This tool utilizes information across multiple time series naturally
— both across the time series and across time, which they describe in their new paper.
The recent publication also discusses attractive alternatives,
where instead of transforming the multivariate time series into a big matrix, it is viewed as a three-dimensional tensor.
Their algorithm outperformed all the others on imputation,
and it exceeded all but one of the other algorithms when it came to forecasting future values.
The researchers also demonstrated that their algorithm can be applied to all time-series data.
The system could enable a non-expert to predict future stock prices with high accuracy in just a few minutes,
even if the time-series dataset contains missing values.
The researchers have shown why the algorithm works so well. These algorithms automatically enable change point detection.
If the user believes their time series will change its behavior at some point,
the system will automatically detect that change and incorporate that into its predictions.
The work provides a practical algorithm that requires no hand-tuning; it demonstrates the effective values by being competitive
with or outperforming several known algorithms for imputations and predictions in time series for several real-world data sets.
At the heart of it all is the beautiful modeling work where they cleverly exploit correlations across time (within a time series)
and space (across time series) to create a low-rank spatiotemporal factor representation of a multivariate time series.
Importantly this model connects the field of time series analysis to the rapidly evolving topic of tensor completion,
which will expect a lot of follow-on research spurred by the work.