如果與data.frame中的apply一起運行，則auto.arima結果不匹配

Question

摘要：我需要預測時間序列的25個變量，但是在逐一運行與套用之間結果不匹配：

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

VS

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

示例數據集和腳本：我的原始數據是某些快速消費品的消費趨勢，但由於數據隱私問題我無法分享，因此我准備了土耳其消費者價格指數趨勢的3個組成部分。 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

這是我只有一個的結果：

> 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

這是食物的結果，我用以下方法生產：

> 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

任何幫助將不勝感激，在此先感謝，塞爾丘克

Answer 1

使用apply會丟失所有時間序列屬性。 在這種情況下，您會丟失有關季節性的信息。 因此auto.arima不知道數據是季節性的，因此無法考慮周期為12的季節性模型。

您可以按以下步驟解決問題：

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) )