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By Krishna Prabu, Shikka Maheshwari, Vishal Chahal | Published September 11, 2017 - Updated September 11, 2017
In statistical analysis, time series data consists of a sequence of data points that you can monitor and analyze over time: stock market closing values, daily high and low temperatures, social media posts. This developer journey shows you how to use IoT sensor data, IBM Watson Studio, and R software for statistical computing to analyze the data and detect change points.
The world is awash in data. More and more devices are now connected, whether for social interactions or remote monitoring and management. Auto manufacturing, weather forecasting, power grids – no aspect of our lives remains untouched by the Internet of Things. Gone are the days when a technician needed to inspect a rooftop air conditioning unit onsite to physically identify issues. No longer does a manufacturer have to wait for a finished part to manually pass a quality test to find defects.
But how do we predict failing equipment in a remote store thousands of miles away? Can we predict equipment failure and initiate a corrective action before any breakdowns occur? And can we provide those proactive responses in real time? Can we overcome the challenges of geographic distribution to improve our work and personal lives? The answer, of course, is yes.
Developers need to know how to find that kind of insight in data. This code pattern shows you how to detect when a change point occurs in time series data from an IoT sensor. You will compute statistical parameters from time series data, comparing a data set for a previous time range with a current time range. The statistical comparison between these two data sets enables you to detect any change points. You’ll use the R statistical analysis project and sample sensor data that you will load into the IBM Watson Studio cloud.
When you complete this pattern, you will understand how to:
Ready to put this code pattern to use? Complete details on how to get started running and using this application are in the README.
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