Peak Detection in Time Series

Question

I\'m currently working on a little project in which I want to compare two time-series. The similarity measure is really vague, they are considered to be similar if the two time

Answer 1

You seem to simply look for slope inversion (from positive to negative and vice versa). A rough java algo could be (not tested):

List<Point> points = ... //all the points in your curve
List<Point> extremes = new ArrayList<Point> ();
double previous = null;
double previousSlope = 0;

for (Point p : points) {
    if (previous == null) { previous = p; continue; }
    double slope = p.getValue() - previous.getValue();
    if (slope * previousSlope < 0) { //look for sign changes
        extremes.add(previous);
    }
    previousSlope = slope;
    previous = p;
}

Finally, a good way to measure similarity is correlation. In your case, I would look at % move correlation (in other words, you want your 2 series to go up or down at the same time) - that's typically what is done in finance where you calculate the correlation between 2 assets returns for example:

• create 2 new series with the % move for each point of the 2 series
• calculate the correlation between those 2 series

You can read more about returns correlations here for example. In summary, if your values are:

Series 1  Series 2
 100        50
 98         49
 100        52
 102        54

The "returns" series will be:

Series 1  Series 2
 -2.00%     -2.00%
 +2.04%     +6.12%
 +2.00%     +3.85%

And you calculate the correlation of those 2 returns series (in this example: 0.96) to get a measure of how much the 2 curves look alike. You might want to adjust the result for variance (i.e. if one shape has a much wider range than the other).

Answer 2

If you want something statistically more sound, you could measure the cross correlation between the two series. You can check Wikipedia, or this site.

Answer 3

I'm not sure about correlation between time series or specific peak detection algorithms but here's a little maximum peak detection algorithm I wrote. It doesn't detect the minimum peaks but could easily be extended to do so by reversing the operations in the for loop.

List<XYDataItem> maxPoints = ... //list to store the maximums
XYDataItem leftPeakPoint = new XYDataItem(0, 0);
int leftPeakPointIndex = 0;
XYDataItem rightPeakPoint = new XYDataItem(0, 0);
boolean first = true;
int index = -1;
List<XYDataItem> pointList = (List<XYDataItem>) lrpSeries.getItems();
for (XYDataItem point : pointList) {
    index++;
    if (first) {
        //initialize the first point
        leftPeakPoint = point;
        leftPeakPointIndex = index;
        first = false;
        continue;
    }
    if (leftPeakPoint.getYValue() < point.getYValue()) {
        leftPeakPoint = point;
        leftPeakPointIndex = index;
        rightPeakPoint = point;
    } else if (leftPeakPoint.getYValue() == point.getYValue()) {
        rightPeakPoint = point;
    } else {
        //determine if we are coming down off of a peak by looking at the Y value of the point before the
        //left most point that was detected as a part of a peak
        if (leftPeakPointIndex > 0) {
            XYDataItem prev = pointList.get(leftPeakPointIndex - 1);
            //if two points back has a Y value that is less than or equal to the left peak point
            //then we have found the end of the peak and we can process as such
            if (prev.getYValue() <= leftPeakPoint.getYValue()) {
                double peakx = rightPeakPoint.getXValue() - ((rightPeakPoint.getXValue() - leftPeakPoint.getXValue()) / 2D);
                maxPoints.add(new XYDataItem(peakx, leftPeakPoint.getYValue()));
            }
        }
        leftPeakPoint = point;
        leftPeakPointIndex = index;
        rightPeakPoint = point;
    }
}

The result of this will center the detected peak on flat sections where the Y value of consecutive data points is the same. XYDataItem is just a class that contains an X and Y value as a double. This can easily be replaced with something equivalent.

Answer 4

Late answer for the question but Dynamic Time Warping (DTW) algorithm is the right choise for this type problems. Basicly there is a two time series one of them is template other one is sample. I recomment to check source code of Smile libraries DynamicTimeWarping class.

http://haifengl.github.io/

Answer 5

The peakdet algorithm as proposed by Eli Billauer works very well and is easy to implement:

http://www.billauer.co.il/peakdet.html

The algorithm works especially well with noisy signals where methods using the first derivative fail.

Answer 6

You can use a very simple local extremes detector:

// those are your points:
double[] f = {1, 2, 3, 4, 5, 6, 5, 4, 7, 8, 9, 3, 1, 4, 6, 8, 9, 7, 4, 1};
List<Integer> ext = new ArrayList<Integer> ();
for (int i = 0; i<f.length-2; i++) {
  if ((f[i+1]-f[i])*(f[i+2]-f[i+1]) <= 0) { // changed sign?
    ext.add(i+1);
  }
}
// now you have the indices of the extremes in your list `ext`

This will work nice with smooth series. If you have a certain variation in your data, you should put it through a low pass filter first. A very simple implementation of a low pass filter would be the moving average (every point is replaced by the average of the nearest k values, with k being the window size).