The AnomalyDetector operator in the time series toolkit can be an invaluable tool for detecting anomalies in real-time. If you are not familiar with the AnomalyDetector operator, please take a look at this article first: Anomaly Detection in Streams. In summary, this operator works by comparing the current time series data with a reference pattern (the reference pattern is updated... Continue reading Detecting Anomalies in Seasonal Data

The STD2 operator is capable of performing online decomposition of a time series. More specifically, the STD2 operator is capable of ingesting a time series and decomposing it into seasonal, trend and residual components.
To better understand what these components mean, take a look at the following signal:
The above signal contains some very obvious characteristics.... Continue reading Real-Time Decomposition of Time Series

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... Continue reading Predicting the Future in a Streams Application

This article demonstrates how to use the AnomalyDetector operator, which is capable of detecting anomalous subsequences in a streaming time series. Continue reading Anomaly Detection in Streams

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

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