l***7
发帖数: 50 | 1 我有一组数据e1x1要生成ARIMA model.
44.7669 41.10659 43.70411 57.4064 47.21011
42.29574 44.65526 58.10271 50.38049 45.50983
47.45789 58.99774 53.02062 48.50563 52.39574
61.41254 56.42799 50.43014 57.19581 63.28712
59.45977 52.45281 59.88543 64.37775 60.75301
52.53201 61.68126 66.98495 63.95032 54.09921
64.57885 69.79277 67.75543 55.75373 65.93788
72.11951 70.56782 57.83367 67.67668 75.37291
72.30631 60.17343 69.73671 77.50877 74.87953
60.94993 72.871 79.53782 75.46017 62.1249
74.34622 81.8886 76.78092 63.26446 77.48085
82.91661 78.22325 65.0574 79.46567 84.63805
78.68579 65.11675 80.77741 88.73077 80.60261
66.06199 81.7418 91.10974 82.64659 66.65171
82.32291 94.00293 85.78439 68.92323 83.46343
95.93818
我用了下面的分析
> e1x1=scan("e1x1.dat")
Read 76 items
> e1x1=ts(e1x1)
> plot(e1x1, type="b")
The plot 显示 upward trend and seasonality.
因为 upward trend， 我们可以用 first difference to detrend the data. 因为
the seasonality, 我们可以用the fourth difference can be used to detrend the
first differenced data.
> diff1=diff(e1x1,1)
> diff1and4=diff(diff1,4)
> library(astsa)
> acf2(diff1and4,60)
但是ACF和 PACF 很weird!
大侠指点一下迷津吧！
谢谢！谢谢！ |
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