Examples

use com.ibm.streams.timeseries.modeling::FMPFilter;
composite Main
{
graph
	stream<rstring Date, float64 Open> stockStream = FileSource() {
		param
			file: "stock.csv";  //data of the IBM stock
	 		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;
	}
}

use com.ibm.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();
	}
}