IoT Developers May Need Time Series Data Analysis Skills

The location of analytics execution and the available storage capacity and processing power are two factors developers must address, he noted. These days that can mean deploying analytics on edge computing systems, on-premises or on the cloud.  

MathWorks MATLAB software, Lluch said, enables developers to create time series data analysis independently of the platform on which it may execute.  The software also allows developers to work with new artificial intelligence-style machine learning and predictive analytics models, without learning separate data science tools.

Care and Feeding of Neurals

Today’s IoT developers are familiar with AI and deep neural networks and believe they must employ such approaches. But their results will vary depending on what type of data the sensors pick up.  

That’s according to Chris Rogers, chief executive officer at SensiML, maker of a platform for edge computing and IoT data analysis. He said simpler machine learning tools, some that automate portions of the development task, may prove more useful some in instances.

As an example, he pointed to repeatable factory line visual inspection tasks, which, he said, can use simpler machine learning pattern recognition methods that have easier model training requirements.

“For time series data streams — from accelerometers, microphones, strain gauges, pressure sensors or load cells — classic machine learning algorithms can often prove a better fit, requiring much less training and test data, fitting in a smaller footprint, and requiring much less computing power to implement,” Rogers said.  

Time Series Methods Merging

Time series data analysis is now seeing a useful merging of methods, according to Rosaria Silipo, a principal data scientist at KNIME, which is a maker of an open source data analytics platform.

“One branch comes from statistics, with [Autoregressive Integrated Moving Average, or ARIMA] and its statistical requirements, and the other comes from machine learning, with looser requirements and more powerful algorithms,” she said. 

The two branches are still in the process of merging, she advised, and this is generating “quite a disorienting feeling” for developers who are analyzing time series data. 

Of course, she emphasized, the big driver here is the fact that “IoT sensors generate data at a speed rarely seen before.” 

“Calculation power and speed — possibly real time — have become more and more relevant,” she said, “allowing for a little drop in prediction performance if it brings a noticeable improvement in speed.” IoT developers now wrestle with this trade-off between speed and performance.

 Silipo said the streaming of IoT data is fast, heterogeneous and usually highly dimensional. Successfully creating visualizations of such data — with fast implementation — may require a reduction in dimensionality to represent different data types in the same space. 

“All those things have been possible so far, if taken separately,” she said. “The combination of all of them is the current challenge.”

The Moving Analytics Bottleneck

Overall, the changes in sensor and signaling systems over time is quite remarkable, SensiML’s Rogers said. Since the 1990s when he began his career as an automotive test engineer, key changes have taken place.