setwd("D:/Masters in Climate Change/Spectral Analysis/Data")
library(bspec)
library(lomb)
library(WaveletComp)
library(zoo)
library(quantmod)
library(dplyr)
library(astrochron)
library(IRISSeismic)
library(Hmisc)

Reading in the central england temperature data from csv file.

CET <- read.csv("CET.csv")[ ,14]
New_age <- read.csv("CET.csv")[ ,1]
my.data = data.frame(date = New_age, CET=CET)

Wavelet Transform Analysis

my.wt = analyze.wavelet(my.data, "CET", loess.span = 0, dt = 10, dj = 1/20, make.pval = TRUE,
                        method = "white.noise", params = NULL, n.sim = 100, date.format = "%Y", date.tz = NULL, verbose =
                          TRUE)

Creating the wavelet transform image showing periods and significance along with wavelet power.

wt.image(my.wt, color.key = "i", label.time.axis = TRUE, main = "wavelet power spectrum CET",
         legend.params = list(lab = "wavelet power levels"), periodlab = "period (years)", timelab = "time (years)",
         date.format = "%d", spec.time.axis = list(at = seq(-10, 462, by = 10), labels = seq(0, 460, by = 50))) 
## Warning in wt.image(my.wt, color.key = "i", label.time.axis = TRUE, main = "wavelet power spectrum CET", : 
## Please check your time axis specifications. Default settings were used.

Cycles and occurences

There isnt too many main signiicant cycles that occuring in this image.There seems to be some significant time periods that occur frequently but dont last very long.

The most significant cycle occured around 2,750 years ago and has a frequency level of approximately 15-20 years.

There are other cycles over larger periods and they occur less frequently, however they are less significant.These include large periods 2,250-3,000 years ago with a frequency level of 128-200.The red areas mark the most significant cycles based on the wavelet power level chart on the side.

Plotting average cycles

wt.avg(my.wt, siglvl=0.05, sigcol="red",main = "significance level")