This project showcases how one can make use of the Predictive Analytics service, available on the IBM Bluemix, to determine the hidden patterns on the events published by IoT devices, on the IBM Watson IoT Platform.
Following are the list of components used in this project,
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Device component - publishes the temperature events to the IBM Watson IoT Platform for every 2 seconds.
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Spark streaming component - Multiple receivers running in the Apache Spark service subscribes to these events and make a ReST call to the SPSS model deployed on Predictive Analysis
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SPSS Model - The SPSS stream is built on top of the SPSS streaming time series expert model. Based on the input data, it finds the most suitable time series forecast model and trains the model automatically during the scoring time. The first 50 data points are used for training and the model will adjust itself over time. The stream is deployed in Predictive Analytics service. Through API call, the service will return the next few forecasts based on the input. When real time data reading is received, the Spark streaming job gets the next few forecasts from Predictive Analytics service. It also calculates a A ZScore (aka, a standard score indicates how many standard deviations an element is from the mean) to indicate the degree of difference in the actual reading compared to the forecast readings. Since the forecast is a trend indicator, a bigger difference than the normal range would indicate a sudden change of value.
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RTI component - The ZScore will be used in RTI rule to determine when the alert will be raised. A larger value filters out smaller spikes and dips.
Instruction about how to run the sample can be found in their respective directories.
Refer to the recipe Engage Machine Learning for detecting anomalous behaviors of things for more information about the Predictive Analytics and the samples.
The library is shipped with Eclipse Public License and refer to the [License file] (https://github.com/ibm-messaging/iot-predictive-analytics-samples/blob/master/LICENSE) for more information about the licensing.
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