在嘈杂的时间序列中（在R？中）检测周期最大值（峰值）

问题描述

这个问题是关于确定数字序列中最大值的数量和位置的算法。因此，该问题具有统计意义，但它更倾向于编程，因为我对特定的统计特性不感兴趣，并且解决方案必须在R中。使用统计数据来回答这个问题是可以的，

我想提取时间序列数据（即数字的有序序列）中循环的最大值。这种数据的一个例子是太阳耀斑时间序列（约11年的周期，介于9到14年之间）。循环不会以理想的间隔重复，并且峰的高度并不总是相同。

我发现了一篇最近的论文，描述了为此的算法，并且论文实际上以太阳耀斑为例（图5，Scholkmann等，2012，算法）。我希望该算法或同等有效的算法可以作为R包使用。

链接到Scholkmann的论文基于多尺度的自动峰检测 $ b

This question is about an algorithm for determining the number and location of maxima in a sequence of numbers. Thus, there is a statistical flavor to the question, but it is more leaning towards programming, because I am not interested in the specific statistical properties, and the solution needs to be in R. The use of statistics to answer this question is OK, but not a requirement.

I want to extract maxima of cycles in time series data (i.e., an ordered sequence of numbers). An example of such data is the solar flare time series (~11 year cycle, between 9 & 14 years). The cycles don't repeat at a perfect interval, and the peaks aren't always the same height.

I found a recent paper describing an algorithm for this, and the paper actually uses solar flares as an example (Figure 5, Scholkmann et al. 2012, Algorithms). I was hoping that this algorithm, or an equally effective algorithm, was available as an R package.

Link to Scholkmann paper on "automatic multiscale-based peak detection"http://www.mdpi.com/1999-4893/5/4/588

I've tried the "turningpoints" function in the "pastecs" package but it seemed to be too sensitive (i.e., detected too many peaks). I thought of trying to smooth the time series first, but I'm not sure if this is the best approach (I'm no expert).

Thanks for any pointers.

解决方案

Here is a solution involving the wmtsa package in R. I added my own little function to facilitate the searching of maxima once the wmtsa::wavCWTPeaks got it close.

PeakCycle <- function(Data=as.vector(sunspots), SearchFrac=0.02){
    # using package "wmtsa"
    #the SearchFrac parameter just controls how much to look to either side
    #of wavCWTPeaks()'s estimated maxima for a bigger value
    #see dRange
    Wave <- wavCWT(Data)
    WaveTree <- wavCWTTree(Wave)
    WavePeaks <- wavCWTPeaks(WaveTree, snr.min=5)
    WavePeaks_Times <- attr(WavePeaks, which="peaks")[,"iendtime"]

    NewPeakTimes <- c()
    dRange <- round(SearchFrac*length(Data))
    for(i in 1:length(WavePeaks_Times)){
        NewRange <- max(c(WavePeaks_Times[i]-dRange, 1)):min(c(WavePeaks_Times[i]+dRange, length(Data)))
        NewPeakTimes[i] <- which.max(Data[NewRange])+NewRange[1]-1
    }

    return(matrix(c(NewPeakTimes, Data[NewPeakTimes]), ncol=2, dimnames=list(NULL, c("PeakIndices", "Peaks"))))
}

dev.new(width=6, height=4)
par(mar=c(4,4,0.5,0.5))
plot(seq_along(as.vector(sunspots)), as.vector(sunspots), type="l")
Sunspot_Ext <- PeakCycle()
points(Sunspot_Ext, col="blue", pch=20)

这篇关于在嘈杂的时间序列中（在R？中）检测周期最大值（峰值）的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！