package de.l3s.common.features;
import java.util.Arrays;
import org.rosuda.JRI.REXP;
import org.rosuda.JRI.Rengine;
import de.l3s.common.TextConsole;
public class TimeSeriesFeatures {
Rengine engine;
public TimeSeriesFeatures(){
String[] rOptions = { "--vanilla" };
engine = new Rengine(rOptions, false, new TextConsole());
engine.eval("library(e1071)");
engine.eval("library(lsa)");
}
/**
*
* @param lengthOfPeriod
* @param timeSeries
* @return
*/
public double[] computeAutoCorrel(int lengthOfPeriod, int[] timeSeries) {
engine.assign("data", timeSeries);
engine.eval("data <- ts(data, frequency =" + lengthOfPeriod +")",false);
engine.eval("query_acf = acf(data, lag.max = NULL, plot = FALSE, type = \"correlation\", na.action = na.pass)",false);
REXP x = engine.eval("query_acf$acf");
double[] scores = x.asDoubleArray();
System.out.println(Arrays.toString(scores));
return scores;
}
/**
* get Kurtosis of the TimeSeries
* @param timeSeries
* @return
*/
public double getKurtosisTimeSeries(int[] timeSeries){
engine.assign("data", timeSeries);
REXP x = engine.eval("kurtosis(data)");
double kurtosis = x.asDouble();
return kurtosis;
}
/**
*
* @param timeSeries
* @return
*/
public double[] decomposeTSSeasonalityfromNYT(int[] timeSeries){
engine.assign("data", timeSeries);
engine.eval("dataTs = ts(data,frequency=365,start=c(1987,01,01),end=c(2007,06,19))");
engine.eval("dataTsdecompose = decompose(dataTs)");
engine.eval("seasonal_acf = acf(dataTsdecompose$seasonal, lag.max = NULL, plot = FALSE, type = \"correlation\", na.action = na.pass)");
REXP x = engine.eval("seasonal_acf$acf");
double[] scores = x.asDoubleArray();
return scores;
}
/**
* get seasonality value of the timeSeries
* @param timeSeries
* @return
*/
public double decomposeTSSeasonalityfromNYTwithCosineSim(int[] timeSeries){
engine.assign("data", timeSeries);
engine.eval("dataTs = ts(data,frequency=12,start=c(1987,01))");
engine.eval("dataTsdecompose = decompose(dataTs)");
engine.eval("seasonal_vector = as.numeric(dataTsdecompose$seasonal)");
REXP x = engine.eval("cosine(data,seasonal_vector)");
return x.asDouble();
}
/**
* get seasonality value of the timeSeries
* @param timeSeries
* @return
*/
public double decomposeTSSeasonalityfromQLwithCosineSim(int[] timeSeries){
engine.assign("data", timeSeries);
engine.eval("dataTs = ts(data,frequency=3)");
engine.eval("dataTsdecompose = decompose(dataTs)");
engine.eval("seasonal_vector = as.numeric(dataTsdecompose$seasonal)");
REXP x = engine.eval("cosine(data,seasonal_vector)");
return x.asDouble();
}
/**
* detect surprise (SSE on prediction model) short timeseries
* @param timeSeries
* @return
*/
public double exponentialSmoothingQLTimeseries(int[] timeSeries){
engine.assign("data", timeSeries);
engine.eval("data_ts = ts(data,frequency=3)");
engine.eval("data_ts_forecasts = HoltWinters(data_ts, gamma=FALSE)");
REXP x = engine.eval("data_ts_forecasts$SSE");
return x.asDouble();
}
public void endEngine(){
engine.end();
}
public static void main(String[] args) {
TimeSeriesFeatures eval = new TimeSeriesFeatures();
int[] sample_NYT = {1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 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int[] april_fool_ts = {2,3,2,1,0,5,1,0,6,0,0,0,5,0,3,10,3,11,4,1,5,2,7,2,8,4,37,9,9,20,169,337,22,2,9,3,0,3,1,7,0,0,1,1,1,2,0,3,2,2,0,0,1,0,3,1,0,2,0,3,0,1,0,1,0,0,0,3,1,0,0,6,0,0,0,3,0,0,1,0,1,0,0,3,0,0,0,0,0,0,0,0};
int[] american_idol_ts = {359,581,470,178,67,88,173,442,533,505,153,190,122,262,825,510,135,94,68,144,257,694,479,207,192,110,113,209,643,482,105,55,85,71,165,681,463,139,74,83,60,194,345,240,120,59,34,65,214,696,465,70,61,71,59,270,648,450,120,56,84,71,206,762,514,125,89,89,129,266,1244,1149,384,198,169,226,412,12,538,196,137,118,188,393,716,421,159,85,50,97,51,33};
System.out.println(sample_NYT.length);
System.out.println(eval.getKurtosisTimeSeries(american_idol_ts));
System.out.println(eval.getKurtosisTimeSeries(april_fool_ts));
System.out.println("seasonality:"+ Arrays.toString(eval.decomposeTSSeasonalityfromNYT(sample_NYT)));
System.out.println("Seasonality: " + eval.decomposeTSSeasonalityfromNYTwithCosineSim(sample_NYT));
eval.endEngine();
}
}