如果与data.frame中的apply一起运行，则auto.arima结果不匹配

Question

摘要：我需要预测时间序列的25个变量，但是在逐一运行与套用之间结果不匹配：

cpi_fit <- auto.arima(cpi_ts[,1])

VS

cpi_fit_ply <- apply(cpi_ts, 2, function(x) auto.arima(x) )

示例数据集和脚本：我的原始数据是某些快速消费品的消费趋势，但由于数据隐私问题我无法分享，因此我准备了土耳其消费者价格指数趋势的3个组成部分。 食物，衣服和鞋子

cpi <- data.frame( food = c(93.43 , 96.25 , 101.29 , 102.78 , 103.73 , 101.11 , 98.91 , 97.38 , 98.55 , 100.03 , 102.76 , 103.78 , 104.88 , 106.29 , 108 , 108.28 , 106.43 , 103.46 , 103.72 , 104.58 , 105.1 , 108.32 , 109.81 , 110.3 , 111.72 , 112.75 , 112.85 , 111.65 , 111.63 , 109.66 , 108.24 , 108.11 , 109.6 , 112.21 , 116.11 , 115.7 , 117.28 , 120.25 , 121.91 , 122.69 , 123.61 , 119.97 , 121.6 , 119.91 , 123.52 , 124.5 , 128.07 , 128.83 , 134.85 , 137.07 , 137.92 , 139.28 , 136.85 , 133.97 , 132.91 , 135.21 , 138.41 , 143.41 , 144.2 , 144.69 , 147.62 , 155.47 , 157.05 , 158.63 , 158.99 , 153.07 , 153.67 , 153.56 , 153.89 , 160.24 , 161.4 , 162.16 , 164.64 , 164.59 , 171.44 , 170.75 , 170.99 , 168.06 , 166.87 , 164.06 , 164.4 , 169.54 , 173.52 , 177.59 , 180.56 , 190.24 , 191.5 , 191.72 , 182.72 , 177.71 , 176.19 , 181.67 , 190.8 , 199.96 , 195.91 , 190.36 , 193.58 , 198.74 , 197.84 , 196.83 , 206.72 , 192.48 , 189.91 , 192.64 , 194.55 , 202.83 , 209.76 , 214.03 , 216.51 , 221.34 , 220.36 , 219.95 , 214.22 , 206.24 , 207 , 210.2 , 215.02 , 218.76 , 218.29 , 221.92 , 231.4 , 233.57 , 238.59 , 235.25 , 229.64 , 234.09 , 234.64 , 232.65 , 234.37 , 244.15 , 240.52 , 244.14 , 257.57 , 257.79 , 263.46)
                   ,clothes = c(95.41 , 93.4 , 92.89 , 96.69 , 100.51 , 101.34 , 99.49 , 97.07 , 100.3 , 105.25 , 109.01 , 108.65 , 105.01 , 100.67 , 99.11 , 101.8 , 107.45 , 108.46 , 103.83 , 100.9 , 102.64 , 111.09 , 116.34 , 116.11 , 109.61 , 101.85 , 97.89 , 105.61 , 117 , 118.21 , 108.62 , 102.94 , 103.08 , 111.17 , 114.87 , 113.65 , 106.36 , 96.99 , 94.05 , 103.51 , 117.46 , 118.84 , 109.99 , 102.1 , 102.6 , 112.98 , 118.89 , 116.12 , 105.82 , 98.62 , 97.56 , 108.56 , 123.53 , 125.32 , 115.14 , 106.83 , 106.86 , 115.66 , 120.89 , 119.06 , 107.13 , 99.43 , 100.53 , 112.1 , 128.6 , 128.23 , 117.98 , 110.59 , 109.4 , 118.68 , 121.98 , 116.98 , 107.1 , 101.11 , 100.87 , 111.61 , 126.64 , 126.24 , 119.23 , 112.57 , 109.36 , 118.82 , 124.76 , 121.25 , 111.21 , 105.21 , 105.44 , 117.18 , 132.78 , 133.73 , 126.87 , 120.4 , 116.19 , 125.01 , 130.13 , 127.58 , 116.96 , 111.05 , 111.67 , 124.44 , 141.86 , 143.16 , 136.14 , 129.68 , 123.99 , 133.72 , 141.77 , 138.06 , 127.07 , 121.74 , 122.4 , 139.15 , 153.43 , 151.74 , 145.23 , 138.92 , 133.66 , 142.77 , 152.47 , 149.51 , 138.63 , 130.09 , 130.55 , 148.12 , 162.69 , 159.35 , 151.72 , 146.12 , 140.89 , 155.66 , 161.03 , 156.01 , 144.03 , 136.96 , 139.27)
                   , shoes = c(94.75 , 94.41 , 94.4 , 97.83 , 99.97 , 99.95 , 99.35 , 98.95 , 101.37 , 104.39 , 107.39 , 107.24 , 105.92 , 103.39 , 101.08 , 104.01 , 108.32 , 109.1 , 106.68 , 103.7 , 106.79 , 112.8 , 117.17 , 118.23 , 114.09 , 106.78 , 104.85 , 115.87 , 120.16 , 120.38 , 113.66 , 108.45 , 112.66 , 119.04 , 123.94 , 124.78 , 117.56 , 109.86 , 106.95 , 114.28 , 118.35 , 118.12 , 111.81 , 104.94 , 113.6 , 123.13 , 127.53 , 126.2 , 117.25 , 110.79 , 113.08 , 126.01 , 131.58 , 132.65 , 124.11 , 115.59 , 123.1 , 133.12 , 137.98 , 137.32 , 122.48 , 114.93 , 119.37 , 133.97 , 139.48 , 138.33 , 127.25 , 120.43 , 127.12 , 137.8 , 140.81 , 136.09 , 125.79 , 118.84 , 120.03 , 134.26 , 141.53 , 138.21 , 128.17 , 123.04 , 129.29 , 139.06 , 143.49 , 139.58 , 128.47 , 122.37 , 125.05 , 136.11 , 141.95 , 140.64 , 133.03 , 128.14 , 131.11 , 141.25 , 145.7 , 144 , 135.46 , 128.06 , 129.92 , 141.91 , 147.96 , 147.46 , 140.66 , 136.49 , 137.52 , 149.17 , 155.41 , 154.36 , 142.61 , 137.35 , 141.06 , 157.62 , 163.14 , 163.31 , 155.66 , 147.19 , 148.98 , 159.12 , 166.53 , 166.42 , 156.63 , 145.67 , 149.24 , 164.96 , 174.38 , 173.16 , 165.61 , 160.04 , 163.12 , 174.13 , 178.82 , 177.29 , 164.32 , 155.23 , 163.05)
                   )

cpi_ts <- ts(cpi, start=c(2003, 1), end=c(2014, 3), frequency=12) 

cpi_fit <- auto.arima(cpi_ts[,1])
cpi_forecast <- forecast(cpi_fit, level=c(99), h=6)

cpi_fit
cpi_forecast

cpi_fit_ply <- apply(cpi_ts, 2, function(x) auto.arima(x) )
cpi_forecast_ply <- apply(cpi_ts, 2, function(x) forecast(auto.arima(x), level=c(99), h=6) )

cpi_fit_ply$food
cpi_forecast_ply$food

这是我只有一个的结果：

> cpi_fit
Series: cpi_ts[, 1] 
ARIMA(2,1,2)(1,0,1)[12] with drift         

Coefficients:
         ar1      ar2      ma1   ma2    sar1     sma1   drift
      1.3425  -0.6305  -1.4266  0.57  0.9430  -0.7661  1.1721
s.e.     NaN      NaN      NaN   NaN  0.0473   0.1061  0.3132

sigma^2 estimated as 10.38:  log likelihood=-347.13
AIC=710.26   AICc=711.41   BIC=733.44
> cpi_forecast
         Point Forecast    Lo 99    Hi 99
Apr 2014       261.3114 253.0115 269.6112
May 2014       258.2686 247.0133 269.5239
Jun 2014       254.2368 241.4043 267.0692
Jul 2014       253.6520 239.9956 267.3085
Aug 2014       254.6313 240.5144 268.7482
Sep 2014       257.5707 243.1288 272.0125

这是食物的结果，我用以下方法生产：

> cpi_fit_ply$food
Series: x 
ARIMA(4,1,3) with drift         

Coefficients:
         ar1     ar2      ar3      ar4      ma1      ma2     ma3   drift
      0.8093  0.4999  -0.7247  -0.0669  -0.8446  -0.7004  0.8292  1.1936
s.e.  0.1217  0.1617   0.1398   0.1015   0.0892   0.1221  0.0831  0.1706

sigma^2 estimated as 11.13:  log likelihood=-349.82
AIC=717.64   AICc=719.1   BIC=743.72
> cpi_forecast_ply$food
    Point Forecast    Lo 99    Hi 99
136       263.1505 254.5564 271.7446
137       260.3486 248.4072 272.2900
138       258.1259 244.6140 271.6377
139       255.3471 240.7681 269.9260
140       254.6141 239.6324 269.5957
141       255.0057 239.7582 270.2532

任何帮助将不胜感激，在此先感谢，塞尔丘克

Answer 1

使用apply会丢失所有时间序列属性。 在这种情况下，您会丢失有关季节性的信息。 因此auto.arima不知道数据是季节性的，因此无法考虑周期为12的季节性模型。

您可以按以下步骤解决问题：

cpi_fit_ply <- apply(cpi_ts, 2, 
         function(x) auto.arima(ts(x,start=c(2003, 1), 
                  end=c(2014, 3), frequency=12) ) )
cpi_forecast_ply <- apply(cpi_ts, 2, 
         function(x) forecast(auto.arima(ts(x,start=c(2003, 1), 
                  end=c(2014, 3), frequency=12)), level=c(99), h=6) )