Created
April 10, 2019 04:42
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Smoothed z-score algorithm. Peak detection with robust threshold.
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public class ZScoreOutput | |
{ | |
public List<double> input; | |
public List<int> signals; | |
public List<double> avgFilter; | |
public List<double> filtered_stddev; | |
} | |
public static class ZScore | |
{ | |
public static ZScoreOutput StartAlgo(List<double> input, int lag, double threshold, double influence) | |
{ | |
// init variables! | |
int[] signals = new int[input.Count]; | |
double[] filteredY = new List<double>(input).ToArray(); | |
double[] avgFilter = new double[input.Count]; | |
double[] stdFilter = new double[input.Count]; | |
var initialWindow = new List<double>(filteredY).Skip(0).Take(lag).ToList(); | |
avgFilter[lag - 1] = Mean(initialWindow); | |
stdFilter[lag - 1] = StdDev(initialWindow); | |
for (int i = lag; i < input.Count; i++) | |
{ | |
if (Math.Abs(input[i] - avgFilter[i - 1]) > threshold * stdFilter[i - 1]) | |
{ | |
signals[i] = (input[i] > avgFilter[i - 1]) ? 1 : -1; | |
filteredY[i] = influence * input[i] + (1 - influence) * filteredY[i - 1]; | |
} | |
else | |
{ | |
signals[i] = 0; | |
filteredY[i] = input[i]; | |
} | |
// Update rolling average and deviation | |
var slidingWindow = new List<double>(filteredY).Skip(i - lag).Take(lag+1).ToList(); | |
var tmpMean = Mean(slidingWindow); | |
var tmpStdDev = StdDev(slidingWindow); | |
avgFilter[i] = Mean(slidingWindow); | |
stdFilter[i] = StdDev(slidingWindow); | |
} | |
// Copy to convenience class | |
var result = new ZScoreOutput(); | |
result.input = input; | |
result.avgFilter = new List<double>(avgFilter); | |
result.signals = new List<int>(signals); | |
result.filtered_stddev = new List<double>(stdFilter); | |
return result; | |
} | |
private static double Mean(List<double> list) | |
{ | |
// Simple helper function! | |
return list.Average(); | |
} | |
private static double StdDev(List<double> values) | |
{ | |
double ret = 0; | |
if (values.Count() > 0) | |
{ | |
double avg = values.Average(); | |
double sum = values.Sum(d => Math.Pow(d - avg, 2)); | |
ret = Math.Sqrt((sum) / (values.Count() - 1)); | |
} | |
return ret; | |
} | |
} |
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var input = new List<double> {1.0, 1.0, 1.1, 1.0, 0.9, 1.0, 1.0, 1.1, 1.0, 0.9, 1.0, | |
1.1, 1.0, 1.0, 0.9, 1.0, 1.0, 1.1, 1.0, 1.0, 1.0, 1.0, 1.1, 0.9, 1.0, 1.1, 1.0, 1.0, 0.9, | |
1.0, 1.1, 1.0, 1.0, 1.1, 1.0, 0.8, 0.9, 1.0, 1.2, 0.9, 1.0, 1.0, 1.1, 1.2, 1.0, 1.5, 1.0, | |
3.0, 2.0, 5.0, 3.0, 2.0, 1.0, 1.0, 1.0, 0.9, 1.0, 1.0, 3.0, 2.6, 4.0, 3.0, 3.2, 2.0, 1.0, | |
1.0, 0.8, 4.0, 4.0, 2.0, 2.5, 1.0, 1.0, 1.0}; | |
int lag = 30; | |
double threshold = 5.0; | |
double influence = 0.0; | |
var output = ZScore.StartAlgo(input, lag, threshold, influence); |
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