Machine Learning Time Series Data

In the past there has been a disproportionate emphasis on vision, sound and writing/language machine learning mainly because these are the things we humans mainly use communicate. However, time series data is also ubiquitous. It’s data that has a time ordering, usually with an associated time stamp. Examples include weather readings, physiological signals, financial data and industrial observations.

To appreciate the scope of time series data, consider that all electronic sensors produce time series data. Industries using sensors include transport, logistics, smart manufacturing, smart cities, automotive, aeronautical, utilities, robotics and health.

Times series can be difficult to analyse because it depends not just on one data point but the relationship between many data points over time. There can be gaps in data and noise that subtly changes the data. The data itself can also be complex in that it can be multi-input, for example accelerometer x,y,z rather than just one value at a time.

Learning the complex structure in time series data requires that patterns , called features, be detected over both short and long sections of data. The data can also vary in average amplitude for analogue data such as acceleration and light and yet we still need to extract the common patterns. Time series data can also arrive at a high data rate. For example, acceleration data can be x,y,z every 100ms (10 times per second) and sometimes faster.

Data produced by sensors isn’t implicitly labelled. That is, there’s nothing classifying the data, as it is produced, that can be used for supervised learning. Instead, any labelling has to be done by hand that can be very time consuming and in many cases isn’t practically possible. Instead, we use unsupervised learning that doesn’t require labelling by humans.

The high visibility of vision, sound and writing/language machine learning has meant that machine learning of time series is less advanced. There has been significant research but much of it isn’t usable on real-world data because many methods aren’t scalable and are hugely computationally intensive. There’s also a lack of knowledge of how to practically implement such machine learning methods. Many organisations also struggle to create data suitable for machine learning.

We are one of a very few companies that have solved the above problems to provide Edge devices that perform machine learning of sensor time series data.