Examples
These examples demonstrate how to use the FMPFilter operator.
The following example produces a smoothed trend of the IBM stock prices and flags unusual events by using the FMPFilter operator:
use com.teracloud.streams.timeseries.modeling::FMPFilter;
composite Main {
graph
stream<rstring date, float64 open> stockStream = FileSource() {
param
file: "stock.csv";
format: csv;
initDelay: 1.0f;
}
stream<list<float64> prediction, list<int32> flags> trendStream = FMPFilter(stockStreams) {
param
inputTimeSeries: open; // input data
degree: 2u; // use a polynomial of degree 2
memoryLength: 20u; // use 20 samples as history lag (polynomial window)
integration: 10u; // use the past 10 values for estimating the variance
thresholdFactor: 7u; // threshold rate of the anomaly detection
maxDimension: 40u; // support up to 25 component in the input data
cleanFrom: 25u; // remove all anomalies from the data after 25th received tuple.
output
trendStream: prediction = predictedTimeSeries(), // get smoothed trend
flags = anomalousFlags(); // get outlier (1 positive outlier and 0 negative outlier)
}
() as SinkOp1 = FileSink(trendStream) {
param
file: "trendStream.dat";
format: csv;
flush: 1u;
writePunctuations: true;
}
}
The following graph illustrates the prediction (trend) of stock prices by using the FMPFilter operator. The anomaly flag has a value of 1 when unpredictable data occurs; it has a negative value when the data is not anomalous.
The following example detects anomalous computer memory usage by using the FMPFilter operator:
use com.teracloud.streams.timeseries.modeling::FMPFilter;
composite Main {
graph
stream<uint64 tstamp, list<float54> inputdata> datacenter = FileSource() {
param
file: "datacenter.csv";
format: csv;
}
stream<list<float64> inputdata, list<int32> prediction,
list<boolean> flags> predictedData = FMPFilter(datacenter) {
param
inputTimeSeries: inputdata;
degree: 2u;
memoryLength: 50u;
integration: 3u;
thresholdFactor: 2.5u;
maxDimension: 25u;
output
predictedData: prediction = predictedTimeSeries(),
flags = anomalousFlags();
}
}
The following graph shows a time series that simulates memory consumption from a computer. The FMPFilter operator is used to track memory consumption and detect anomalies:
