CODEWITHSUNDEEP
rplot
jtanman
jtanman

Reputation: 684

Autocorrelation plot for only negative values

I would like to do an acf plot in R for only the negative values of a time series. I cannot do this by just subsetting the data for only negative values beforehand, because then the autocorrelation will remove arbitrary number of positive days in between the negative values and be unreasonably high, but rather, I would like to run the autocorrelation on the whole time series and then filter out the results given the first day is negative.

For example, in theory, I could make a data frame with the original series and all of the lagged time series in a data frame, then filter for the negative values in the original series, and then plot the correlations. However, I would like to automate this using the acf plot.

Here is an example of my time series:

> dput(exampleSeries)
c(0, 0, -0.000687, -0.004489, -0.005688, 0.000801, 0.005601, 
0.004546, 0.003451, -0.000836, -0.002796, 0.005581, -0.003247, 
-0.002416, 0.00122, 0.005337, -0.000195, -0.004255, -0.003097, 
0.000751, -0.002037, 0.00837, -0.003965, -0.001786, 0.008497, 
0.000693, 0.000824, 0.005681, 0.002274, 0.000773, 0.001141, 0.000652, 
0.001559, -0.006201, 0.000479, -0.002041, 0.002757, -0.000736, 
-2.1e-05, 0.000904, -0.000319, -0.000227, -0.006589, 0.000998, 
0.00171, 0.000271, -0.004121, -0.002788, -9e-04, 0.001639, 0.004245, 
-0.00267, -0.004738, 0.001192, 0.002175, 0.004666, 0.006005, 
0.001218, -0.003188, -0.004363, 0.000462, -0.002241, -0.004806, 
0.000463, 0.000795, -0.005715, 0.004635, -0.004286, -0.008908, 
-0.001044, -0.000842, -0.00445, -0.006094, -0.001846, 0.005013, 
-0.006599, 0.001914, 0.00221, 6.2e-05, -0.001391, 0.004369, -0.005739, 
-0.003467, -0.002103, -0.000882, 0.001483, 0.003074, 0.00165, 
-0.00035, -0.000573, -0.00316, -0.00102, -0.00144, 0.003421, 
0.005436, 0.001994, 0.00619, 0.005319, 7.3e-05, 0.004513)

Upvotes: 0

Views: 983

Answers (3)

jtanman
jtanman

Reputation: 684

Yea I ended up writing my own functions and just replacing the values in the R acf object with my own values that are just the correlations. So:

genACF <- function(series, my.acf, lag.max = NULL, neg){

    x <- na.fail(as.ts(series))
    x.freq <- frequency(x)
    x <- as.matrix(x)
    if (!is.numeric(x)) 
        stop("'x' must be numeric")
    sampleT <- as.integer(nrow(x))
    nser <- as.integer(ncol(x))

    if (is.null(lag.max)) 
        lag.max <- floor(10 * (log10(sampleT) - log10(nser)))
    lag.max <- as.integer(min(lag.max, sampleT - 1L))
    if (is.na(lag.max) || lag.max < 0) 
        stop("'lag.max' must be at least 0")

    if(neg){

        indices <- which(series < 0)

    }else{

        indices <- which(series > 0)

    }

    series <- scale(series, scale = FALSE)  
    series.zoo <- zoo(series)
    for(i in 0:lag.max){

        lag.series <- lag(series.zoo, k = -i, na.pad = TRUE)
        temp.corr <- cor(series.zoo[indices], lag.series[indices], use = 'complete.obs', method = 'pearson')
        my.acf[i+1] <- temp.corr

    }
    my.acf[1] <- 0
    return(my.acf)

}

plotMyACF <- function(series, main, type = 'correlation', neg = TRUE){

    series.acf <- acf(series, plot = FALSE)
    my.acf <- genACF(series, series.acf$acf, neg = neg)
    series.acf$acf <- my.acf

    plot(series.acf, xlim = c(1, dim(series.acf$acf)[1] - (type == 'correlation')), xaxt = "n", main = main)
    if (dim(series.acf$acf)[1] < 25){

        axis(1, at = 1:(dim(series.acf$acf)[1] - 1))

    }else{

        axis(1)

    }

}

And I get something like this:enter image description here

Upvotes: 0

DatamineR
DatamineR

Reputation: 9628

I tried to implement your description.

correl <- function(x, lag.max = 10){
  library(dplyr)
  m <- matrix(ncol = lag.max, nrow = length(x))
  for(i in 1:lag.max){
    m[,i] <- lag(x, i)
    }
  m <- m[x<0,]
  res <- apply(m, 2, function(y) cor(y, x[x<0], use = "complete.obs"))
  barplot(res)
}

correl(exampleSeries)

enter image description here

Upvotes: 1

JasonAizkalns
JasonAizkalns

Reputation: 20473

Maybe just write your own function? Something like:

negativeACF <- function(x, num.lags = 10)
{
  n    <- length(x)
  acfs <- sapply(0:num.lags, function(i) cor(x[-i:-1], x[(-n-1+i):-n]))
  names(acfs) <- 0:num.lags
  acfs[acfs < 0]
}

results <- negativeACF(exampleSeries, num.lags=20)
barplot(results)

Negative ACFs

Upvotes: 0

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