问题:使用周期图和R中的FFT检测日常数据中的周期性模式。
问题是如何在R中编码周期图以检测数据中的月度,季度,半年度,年度......周期性模式。换句话说,我需要检测低频周期模式的存在(即:1年=> 2 * pi / 365,6个月=> 4 * pi / 365等)
可重复的例子:
library(weatherData)
w2009=getWeatherForYear("sfo",2009)
w2010=getWeatherForYear("sfo",2010)
w2011=getWeatherForYear("sfo",2011)
w2012=getWeatherForYear("sfo",2012)
w2013=getWeatherForYear("sfo",2013)
w2014=getWeatherForYear("sfo",2014)
w=rbind(w2009,w2010); w=rbind(w,w2011); w=rbind(w,w2012)
w=rbind(w,w2013); w=rbind(w,w2014)
# Next we analyze the periodograms
# This is IMAGE 1
TSA::periodogram(w$Max_TemperatureF)
# Next: I dont really know to use this information
GeneCycle::periodogram(w$Max_TemperatureF)
# Next THIS IS IMAGE 2
stats::spectrum(w$Max_TemperatureF)
# I also tried . This is IMAGE 3
f.data <- GeneCycle::periodogram(tmax)
harmonics <- 1:365
plot(f.data$freq[harmonics]*length(tmax),]
f.data$spec[harmonics]/sum(f.data$spec),
xlab="Harmonics (Hz)", ylab="Amplitute Density", type="h")
读完答案后,我做了:
per <- TSA::periodogram(w$Max_TemperatureF,lwd = 1)
x <- which(per$freq < 0.01)
plot(x = per$freq[x], y = per$spec[x], type="s")
我的问题是这一切意味着什么?我们有季节性周期吗?
答案 0 :(得分:0)
如果您正在寻找很长一段时间(365天),您会发现它的频率非常低
> 1/365
[1] 0.002739726
您实际上可以在此值的第一张图片左侧看到一个峰值。如果要放大,请过滤到较低频率:
per <- TSA::periodogram(w$Max_TemperatureF,lwd = 1)
x <- which(per$freq < 0.01)
plot(x = per$freq[x], y = per$spec[x], type="s")
搜索周期性的另一种方法是自相关估计(acf
):
acf(w$Max_TemperatureF, lag.max = 365*3)
另见季节性分解:
ts1 <- ts(data = w$Max_TemperatureF, frequency = 365)
plot( stl(ts1, s.window = "periodic"))