我是R和时间序列分析的新手。我试图找到较长的(40年)每日温度时间序列的趋势,并尝试采用不同的近似值。第一个只是简单的线性回归,第二个是Loess的时间序列的季节性分解。
在后者看来,季节性成分大于趋势。但是,如何量化趋势?我只想说一说这个趋势有多强。
Call: stl(x = tsdata, s.window = "periodic")
Time.series components:
seasonal trend remainder
Min. :-8.482470191 Min. :20.76670 Min. :-11.863290365
1st Qu.:-5.799037090 1st Qu.:22.17939 1st Qu.: -1.661246674
Median :-0.756729578 Median :22.56694 Median : 0.026579468
Mean :-0.005442784 Mean :22.53063 Mean : -0.003716813
3rd Qu.:5.695720249 3rd Qu.:22.91756 3rd Qu.: 1.700826647
Max. :9.919315613 Max. :24.98834 Max. : 12.305103891
IQR:
STL.seasonal STL.trend STL.remainder data
11.4948 0.7382 3.3621 10.8051
% 106.4 6.8 31.1 100.0
Weights: all == 1
Other components: List of 5
$ win : Named num [1:3] 153411 549 365
$ deg : Named int [1:3] 0 1 1
$ jump : Named num [1:3] 15342 55 37
$ inner: int 2
$ outer: int 0