Introduction to the Spark MLLib Toolkit in IBM Streams V4.1

Ankit Pasricha is the team lead of the IBM Streams Toolkit development team. In his presentation, Ankit will introduce the new Spark MLLib Toolkit that is available in IBM Streams V4.1. This toolkit combines the power of Spark MLLib and the real-time streaming capabilities of Streams. Continue reading Introduction to the Spark MLLib Toolkit in IBM Streams V4.1

Predicting the Future in a Streams Application

Time series forecasting is a very broad subject. The ability to forecast future values is applicable in areas such as sales forecasting, stock market analysis and utilities forecasting (i.e. energy consumption). Forecasting can be a complicated subject as there many different forecasting algorithms, with each algorithm having certain properties that only makes it useful in specific circumstances. This article demonstrates how to easily introduce forecasting into an application using the AutoForecaster operator. Continue reading Predicting the Future in a Streams Application

Bandpass and bandstop filters using the DSPFilter operator

The DSPFilter operator implements a butterworth filter and can be used to isolate frequencies in a time series. For example, a low pass filter can be used to reject all frequencies above a certain point (this point is referred to as the cut-off frequency). Likewise, a high pass filter can be used to reject frequencies […] Continue reading Bandpass and bandstop filters using the DSPFilter operator