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Determining periodicity of a time series using spectrum analysis using R
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| # Where 'ts' is the time series (without transient behavior) | |
| # a <- as.data.frame(cbind(spectrum(ts)$freq,spectrum(ts)$spec)) | |
| # b <- order(a[2], decreasing=TRUE)[1] | |
| # 1/a$V1[b] # !If this is the same as length(ts) then periodicity is 0(zero) | |
| periodicity <- function(ts){ | |
| if (length(unique(ts))==1){ | |
| # No need for spectrum analysis as all values are same. Periodicity is zero. | |
| return(0) | |
| } else { | |
| a <- as.data.frame(cbind(spectrum(ts)$freq,spectrum(ts)$spec)) | |
| b <- order(a[2], decreasing=TRUE)[1] # Find the index of the spec with highest frequency | |
| c <- 1/a$V1[b] # Calculate number of cycles | |
| if (c==length(ts)+1){ | |
| return(0) # Periodicity length is same as ts return periodicity 0 | |
| }else{ | |
| return(1/a$V1[b]) # Return periodicity | |
| } | |
| } | |
| } | |
| # sample time series | |
| ts <- rep(c(1:1),100) # 0 periodicity | |
| ts <- rep(c(1:2),100) # 2 cycle | |
| ts <- rep(c(1:3),100) # 3 cycles | |
| ts <- rep(c(1:4),100) # 4 cycles | |
| ts <- rep(c(1:5),100) # 5 cycles | |
| ts <- rep(c(2:11),100) # 10 cycles | |
| ts <- rnorm(100, 1, 100) # Stochastic | |
| periodicity(ts) |
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