複数時系列データの結合、単位根検定及びARIMA

> library(tseries)
> FirstDate="2013/9/1"
> LastDate="2014/8/31"
> path="C:/Users/username/Desktop/R_Folder"
> setwd(path)
> AllData01<-data.frame(date=seq(as.Date(FirstDate),as.Date(LastDate),by="days"),dummy=1)
> AllData02<-AllData01
> TSeriesDataFileList<-dir(path)
> for(iii in 1:length(TSeriesDataFileList))
+ {
+  dataset<-read.table(file=TSeriesDataFileList[iii],header=TRUE,sep=",")
+  dataset[,1]<-as.Date(dataset[,1])
+  for(ccc in 2:ncol(dataset))
+  {
+   #以下は非数値データ行の削除及びデータの実数化。オリジナルデータに合わせて適宜変更。
+   dataset<-subset(dataset,dataset[,ccc]!=".")
+   dataset<-subset(dataset,is.na(dataset[,ccc])==F)
+   dataset<-subset(dataset,as.Date(FirstDate)<=dataset[,1])
+   dataset<-subset(dataset,as.Date(LastDate)>=dataset[,1])
+   dataset[,ccc]<-as.double(as.character(dataset[,ccc]))
+   #日付列名の統一
+   colnames(dataset)[1]<-"date"
+   #単位根検定 H0:単位根有り
+   p0<-adf.test(dataset[,ccc],k=1)$p.value
+   p1<-adf.test(diff(dataset[,ccc]),k=1)$p.value
+   #出力
+   cat(TSeriesDataFileList[iii],",",colnames(dataset)[ccc],"\n",sep="")
+   cat("  1)単位根検定","\n",sep="")
+   cat("    ・原系列,p値=",p0,"\n",sep="")
+   cat("    ・一次階差,p値=",p1,"\n",sep="")
+   cat("  2)Summary","\n",sep="")
+   cat("    ・原系列","\n",sep="")
+   print(summary(dataset[,ccc]))
+   cat("    ・一次階差","\n",sep="")
+   print(summary(diff(dataset[,ccc])))
+   cat("  3)自己相関","\n",sep="")
+   cat("    ・原系列",sep="")
+   print(acf(dataset[,ccc],plot=F))
+   cat("    ・一次階差",sep="")
+   print(acf(diff(dataset[,ccc]),plot=F))
+   cat("  4)ARIMA","\n",sep="")
+   cat("    ・原系列","\n",sep="")
+   for(d in 0:1){
+     for(p in 0:2){
+       for(q in 0:2){
+         tmp <- arima(dataset[,ccc],order=c(p,d,q),method="ML")
+         cat("     order=(",p,",",d,",",q,"),AIC=",tmp$aic,"\n")
+       }
+     }
+   }
+   cat("\n")
+  }
+  #print(head(dataset,3))
+  cat("\n","\n")
+  AllData01<-merge(AllData01,dataset,by="date",all=T,sort=T) #欠損はNAとして処理
+  AllData02<-merge(AllData02,dataset,by="date",sort=T) #全系列に数値データが存在する日付のみ
+ }
DEXJPUS.csv,VALUE
  1)単位根検定
    ・原系列,p値=0.5720206
    ・一次階差,p値=0.01
  2)Summary
    ・原系列
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  96.94  101.30  102.10  101.60  102.60  105.20 
    ・一次階差
    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
-1.24000 -0.24000  0.04000  0.01835  0.27000  1.06000 
  3)自己相関
    ・原系列
Autocorrelations of series ‘dataset[, ccc]’, by lag

    0     1     2     3     4     5     6     7     8     9    10    11    12    13    14    15    16    17    18 
1.000 0.971 0.941 0.915 0.888 0.862 0.840 0.820 0.801 0.783 0.760 0.737 0.714 0.694 0.677 0.655 0.629 0.605 0.576 
   19    20    21    22    23 
0.541 0.502 0.464 0.423 0.380 
    ・一次階差
Autocorrelations of series ‘diff(dataset[, ccc])’, by lag

     0      1      2      3      4      5      6      7      8      9     10     11     12     13     14     15 
 1.000  0.015 -0.051 -0.045  0.008 -0.065 -0.012 -0.077  0.036  0.096  0.022 -0.013 -0.056 -0.031  0.047  0.109 
    16     17     18     19     20     21     22     23 
-0.047  0.080  0.094  0.084 -0.045  0.017  0.024 -0.155 
  4)ARIMA
    ・原系列
     order=( 0 , 0 , 0 ),AIC= 1018.771 
     order=( 0 , 0 , 1 ),AIC= 747.848 
     order=( 0 , 0 , 2 ),AIC= 573.4677 
     order=( 1 , 0 , 0 ),AIC= 255.8418 
     order=( 1 , 0 , 1 ),AIC= 257.6199 
     order=( 1 , 0 , 2 ),AIC= 259.3242 
     order=( 2 , 0 , 0 ),AIC= 257.6365 
     order=( 2 , 0 , 1 ),AIC= 259.8408 
     order=( 2 , 0 , 2 ),AIC= 261.4687 
     order=( 0 , 1 , 0 ),AIC= 250.1977 
     order=( 0 , 1 , 1 ),AIC= 252.1164 
     order=( 0 , 1 , 2 ),AIC= 253.5663 
     order=( 1 , 1 , 0 ),AIC= 252.1242 
     order=( 1 , 1 , 1 ),AIC= 254.0274 
     order=( 1 , 1 , 2 ),AIC= 254.8829 
     order=( 2 , 1 , 0 ),AIC= 253.5215 
     order=( 2 , 1 , 1 ),AIC= 254.8552 
     order=( 2 , 1 , 2 ),AIC= 256.8399 


 
GOLDAMGBD228NLBM.csv,VALUE
  1)単位根検定
    ・原系列,p値=0.1160808
    ・一次階差,p値=0.01
  2)Summary
    ・原系列
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   1193    1261    1297    1293    1317    1404 
    ・一次階差
    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
-38.5000  -7.7500  -0.5000  -0.4203   6.6250  63.7500 
  3)自己相関
    ・原系列
Autocorrelations of series ‘dataset[, ccc]’, by lag

    0     1     2     3     4     5     6     7     8     9    10    11    12    13    14    15    16    17    18 
1.000 0.947 0.889 0.839 0.792 0.754 0.711 0.667 0.617 0.571 0.545 0.521 0.501 0.488 0.456 0.418 0.384 0.348 0.325 
   19    20    21    22    23    24 
0.311 0.290 0.256 0.217 0.184 0.154 
    ・一次階差
Autocorrelations of series ‘diff(dataset[, ccc])’, by lag

     0      1      2      3      4      5      6      7      8      9     10     11     12     13     14     15 
 1.000  0.051 -0.133  0.006 -0.061  0.021  0.058  0.077  0.035 -0.147 -0.049 -0.060 -0.024  0.045  0.101  0.039 
    16     17     18     19     20     21     22     23 
 0.024 -0.149 -0.150  0.108  0.128  0.064  0.040 -0.086 
  4)ARIMA
    ・原系列
     order=( 0 , 0 , 0 ),AIC= 2585.91 
     order=( 0 , 0 , 1 ),AIC= 2323.043 
     order=( 0 , 0 , 2 ),AIC= 2195.807 
     order=( 1 , 0 , 0 ),AIC= 1969.821 
     order=( 1 , 0 , 1 ),AIC= 1962.479 
     order=( 1 , 0 , 2 ),AIC= 1960.881 
     order=( 2 , 0 , 0 ),AIC= 1962.902 
     order=( 2 , 0 , 1 ),AIC= 1961.259 
     order=( 2 , 0 , 2 ),AIC= 1972.587 
     order=( 0 , 1 , 0 ),AIC= 1951.803 
     order=( 0 , 1 , 1 ),AIC= 1952.865 
     order=( 0 , 1 , 2 ),AIC= 1949.675 
     order=( 1 , 1 , 0 ),AIC= 1953.116 
     order=( 1 , 1 , 1 ),AIC= 1951.433 
     order=( 1 , 1 , 2 ),AIC= 1951.473 
     order=( 2 , 1 , 0 ),AIC= 1950.56 
     order=( 2 , 1 , 1 ),AIC= 1951.711 
     order=( 2 , 1 , 2 ),AIC= 1953.306 


 
nikkei_stock_average_daily_en.csv,Close
  1)単位根検定
    ・原系列,p値=0.266896
    ・一次階差,p値=0.01
  2)Summary
    ・原系列
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  13570   14430   14920   14910   15360   16290 
    ・一次階差
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
-610.70  -88.16    6.96    7.62  124.30  450.10 
  3)自己相関
    ・原系列
Autocorrelations of series ‘dataset[, ccc]’, by lag

    0     1     2     3     4     5     6     7     8     9    10    11    12    13    14    15    16    17    18 
1.000 0.935 0.879 0.819 0.770 0.721 0.675 0.636 0.600 0.562 0.532 0.504 0.480 0.467 0.454 0.442 0.436 0.430 0.412 
   19    20    21    22    23 
0.384 0.349 0.310 0.275 0.248 
    ・一次階差
Autocorrelations of series ‘diff(dataset[, ccc])’, by lag

     0      1      2      3      4      5      6      7      8      9     10     11     12     13     14     15 
 1.000 -0.023  0.043 -0.102 -0.039  0.024 -0.036 -0.010 -0.001 -0.059 -0.034 -0.017 -0.045 -0.015  0.007 -0.064 
    16     17     18     19     20     21     22     23 
-0.004  0.124  0.021  0.076 -0.028 -0.042 -0.105  0.037 
  4)ARIMA
    ・原系列
     order=( 0 , 0 , 0 ),AIC= 3789.294 
     order=( 0 , 0 , 1 ),AIC= 3564.811 
     order=( 0 , 0 , 2 ),AIC= 3435.643 
     order=( 1 , 0 , 0 ),AIC= 3239.951 
     order=( 1 , 0 , 1 ),AIC= 3239.131 
     order=( 1 , 0 , 2 ),AIC= 3239.888 
     order=( 2 , 0 , 0 ),AIC= 3239.131 
     order=( 2 , 0 , 1 ),AIC= 3240.636 
     order=( 2 , 0 , 2 ),AIC= 3241.123 
     order=( 0 , 1 , 0 ),AIC= 3222.107 
     order=( 0 , 1 , 1 ),AIC= 3224.004 
     order=( 0 , 1 , 2 ),AIC= 3225.559 
     order=( 1 , 1 , 0 ),AIC= 3223.995 
     order=( 1 , 1 , 1 ),AIC= 3225.367 
     order=( 1 , 1 , 2 ),AIC= 3226.711 
     order=( 2 , 1 , 0 ),AIC= 3225.525 
     order=( 2 , 1 , 1 ),AIC= 3226.876 
     order=( 2 , 1 , 2 ),AIC= 3222.354 

nikkei_stock_average_daily_en.csv,Open
  1)単位根検定
    ・原系列,p値=0.18935
    ・一次階差,p値=0.01
  2)Summary
    ・原系列
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  13440   14450   14920   14910   15350   16270 
    ・一次階差
    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
-456.700 -105.500   18.390    8.172  120.300  536.200 
  3)自己相関
    ・原系列
Autocorrelations of series ‘dataset[, ccc]’, by lag

    0     1     2     3     4     5     6     7     8     9    10    11    12    13    14    15    16    17    18 
1.000 0.940 0.878 0.816 0.765 0.717 0.669 0.636 0.602 0.568 0.535 0.508 0.486 0.473 0.464 0.448 0.442 0.430 0.415 
   19    20    21    22    23 
0.392 0.362 0.332 0.296 0.265 
    ・一次階差
Autocorrelations of series ‘diff(dataset[, ccc])’, by lag

     0      1      2      3      4      5      6      7      8      9     10     11     12     13     14     15 
 1.000  0.085  0.026 -0.080 -0.042  0.001 -0.111  0.042 -0.022 -0.018 -0.043 -0.064 -0.022 -0.030  0.045 -0.087 
    16     17     18     19     20     21     22     23 
 0.015  0.084  0.017  0.079 -0.020 -0.019 -0.081 -0.026 
  4)ARIMA
    ・原系列
     order=( 0 , 0 , 0 ),AIC= 3787.155 
     order=( 0 , 0 , 1 ),AIC= 3543.187 
     order=( 0 , 0 , 2 ),AIC= 3406.351 
     order=( 1 , 0 , 0 ),AIC= 3202.709 
     order=( 1 , 0 , 1 ),AIC= 3196.033 
     order=( 1 , 0 , 2 ),AIC= 3196.824 
     order=( 2 , 0 , 0 ),AIC= 3195.723 
     order=( 2 , 0 , 1 ),AIC= 3199.768 
     order=( 2 , 0 , 2 ),AIC= 3199.072 
     order=( 0 , 1 , 0 ),AIC= 3180.17 
     order=( 0 , 1 , 1 ),AIC= 3180.43 
     order=( 0 , 1 , 2 ),AIC= 3181.968 
     order=( 1 , 1 , 0 ),AIC= 3180.319 
     order=( 1 , 1 , 1 ),AIC= 3182.281 
     order=( 1 , 1 , 2 ),AIC= 3183.536 
     order=( 2 , 1 , 0 ),AIC= 3182.207 
     order=( 2 , 1 , 1 ),AIC= 3183.896 
     order=( 2 , 1 , 2 ),AIC= 3182.015 

nikkei_stock_average_daily_en.csv,High
  1)単位根検定
    ・原系列,p値=0.2332964
    ・一次階差,p値=0.01
  2)Summary
    ・原系列
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  13610   14530   15020   14990   15420   16320 
    ・一次階差
    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
-491.000  -72.720   -0.080    7.549  101.300  472.300 
  3)自己相関
    ・原系列
Autocorrelations of series ‘dataset[, ccc]’, by lag

    0     1     2     3     4     5     6     7     8     9    10    11    12    13    14    15    16    17    18 
1.000 0.949 0.891 0.834 0.783 0.732 0.688 0.651 0.615 0.580 0.548 0.516 0.492 0.478 0.466 0.454 0.444 0.436 0.416 
   19    20    21    22    23 
0.391 0.359 0.325 0.288 0.260 
    ・一次階差
Autocorrelations of series ‘diff(dataset[, ccc])’, by lag

     0      1      2      3      4      5      6      7      8      9     10     11     12     13     14     15 
 1.000  0.178 -0.006 -0.046 -0.028 -0.067 -0.039  0.020 -0.028 -0.051  0.018 -0.065 -0.101 -0.017 -0.003 -0.032 
    16     17     18     19     20     21     22     23 
-0.026  0.166 -0.008  0.105 -0.008 -0.056 -0.137  0.021 
  4)ARIMA
    ・原系列
     order=( 0 , 0 , 0 ),AIC= 3777.507 
     order=( 0 , 0 , 1 ),AIC= 3518.57 
     order=( 0 , 0 , 2 ),AIC= 3368.457 
     order=( 1 , 0 , 0 ),AIC= 3144.468 
     order=( 1 , 0 , 1 ),AIC= 3136.796 
     order=( 1 , 0 , 2 ),AIC= 3138.693 
     order=( 2 , 0 , 0 ),AIC= 3136.571 
     order=( 2 , 0 , 1 ),AIC= 3138.53 
     order=( 2 , 0 , 2 ),AIC= 3140.367 
     order=( 0 , 1 , 0 ),AIC= 3127.607 
     order=( 0 , 1 , 1 ),AIC= 3121.217 
     order=( 0 , 1 , 2 ),AIC= 3123.177 
     order=( 1 , 1 , 0 ),AIC= 3121.426 
     order=( 1 , 1 , 1 ),AIC= 3123.189 
     order=( 1 , 1 , 2 ),AIC= 3125.198 
     order=( 2 , 1 , 0 ),AIC= 3123.09 
     order=( 2 , 1 , 1 ),AIC= 3123.473 
     order=( 2 , 1 , 2 ),AIC= 3126.932 

nikkei_stock_average_daily_en.csv,Low
  1)単位根検定
    ・原系列,p値=0.1994597
    ・一次階差,p値=0.01
  2)Summary
    ・原系列
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  13410   14340   14830   14820   15290   16180 
    ・一次階差
    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
-606.600  -87.410   19.280    8.018  107.900  530.700 
  3)自己相関
    ・原系列
Autocorrelations of series ‘dataset[, ccc]’, by lag

    0     1     2     3     4     5     6     7     8     9    10    11    12    13    14    15    16    17    18 
1.000 0.943 0.882 0.816 0.764 0.709 0.666 0.627 0.594 0.563 0.533 0.506 0.481 0.464 0.452 0.441 0.437 0.426 0.410 
   19    20    21    22    23 
0.382 0.352 0.317 0.283 0.251 
    ・一次階差
Autocorrelations of series ‘diff(dataset[, ccc])’, by lag

     0      1      2      3      4      5      6      7      8      9     10     11     12     13     14     15 
 1.000  0.115  0.064 -0.133 -0.009 -0.072 -0.023 -0.022 -0.056 -0.027 -0.007 -0.008 -0.044 -0.037 -0.030 -0.046 
    16     17     18     19     20     21     22     23 
 0.007  0.106  0.078  0.025 -0.031 -0.029 -0.052 -0.042 
  4)ARIMA
    ・原系列
     order=( 0 , 0 , 0 ),AIC= 3792.703 
     order=( 0 , 0 , 1 ),AIC= 3556.311 
     order=( 0 , 0 , 2 ),AIC= 3395.551 
     order=( 1 , 0 , 0 ),AIC= 3200.883 
     order=( 1 , 0 , 1 ),AIC= 3192.292 
     order=( 1 , 0 , 2 ),AIC= 3194.236 
     order=( 2 , 0 , 0 ),AIC= 3191.451 
     order=( 2 , 0 , 1 ),AIC= 3198.03 
     order=( 2 , 0 , 2 ),AIC= 3189.851 
     order=( 0 , 1 , 0 ),AIC= 3177.774 
     order=( 0 , 1 , 1 ),AIC= 3176.845 
     order=( 0 , 1 , 2 ),AIC= 3176.513 
     order=( 1 , 1 , 0 ),AIC= 3176.403 
     order=( 1 , 1 , 1 ),AIC= 3178.198 
     order=( 1 , 1 , 2 ),AIC= 3175.25 
     order=( 2 , 1 , 0 ),AIC= 3177.696 
     order=( 2 , 1 , 1 ),AIC= 3176.592 
     order=( 2 , 1 , 2 ),AIC= 3176.604 


 
SP500.csv,VALUE
  1)単位根検定
    ・原系列,p値=0.02618246
    ・一次階差,p値=0.01
  2)Summary
    ・原系列
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   1640    1782    1846    1845    1920    2003 
    ・一次階差
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
-40.700  -4.845   2.050   1.454   9.127  36.160 
  3)自己相関
    ・原系列
Autocorrelations of series ‘dataset[, ccc]’, by lag

    0     1     2     3     4     5     6     7     8     9    10    11    12    13    14    15    16    17    18 
1.000 0.975 0.952 0.929 0.906 0.886 0.866 0.847 0.828 0.809 0.793 0.779 0.767 0.757 0.748 0.738 0.729 0.720 0.709 
   19    20    21    22    23 
0.699 0.686 0.675 0.665 0.649 
    ・一次階差
Autocorrelations of series ‘diff(dataset[, ccc])’, by lag

     0      1      2      3      4      5      6      7      8      9     10     11     12     13     14     15 
 1.000 -0.031 -0.032  0.010 -0.052  0.053 -0.011 -0.063  0.037 -0.089  0.008 -0.006 -0.098 -0.106 -0.017 -0.060 
    16     17     18     19     20     21     22     23 
 0.051  0.071 -0.062  0.018 -0.043 -0.013  0.004 -0.038 
  4)ARIMA
    ・原系列
     order=( 0 , 0 , 0 ),AIC= 2976.786 
     order=( 0 , 0 , 1 ),AIC= 2681.469 
     order=( 0 , 0 , 2 ),AIC= 2511.785 
     order=( 1 , 0 , 0 ),AIC= 1963.943 
     order=( 1 , 0 , 1 ),AIC= 1965.892 
     order=( 1 , 0 , 2 ),AIC= 1962.885 
     order=( 2 , 0 , 0 ),AIC= 1965.894 
     order=( 2 , 0 , 1 ),AIC= 1967.764 
     order=( 2 , 0 , 2 ),AIC= 1972.829 
     order=( 0 , 1 , 0 ),AIC= 1947.248 
     order=( 0 , 1 , 1 ),AIC= 1949.178 
     order=( 0 , 1 , 2 ),AIC= 1951.11 
     order=( 1 , 1 , 0 ),AIC= 1949.18 
     order=( 1 , 1 , 1 ),AIC= 1951.165 
     order=( 1 , 1 , 2 ),AIC= 1952.809 
     order=( 2 , 1 , 0 ),AIC= 1951.108 
     order=( 2 , 1 , 1 ),AIC= 1952.778 
     order=( 2 , 1 , 2 ),AIC= 1946.59 


 
 17 件の警告がありました (警告を見るには warnings() を使って下さい) 
> #TSeriesDataFileList
> #head(AllData01,10)
> #head(AllData02,10)
> 
> 

library(tseries)
FirstDate="2013/9/1"
LastDate="2014/8/31"
path="C:/Users/username/Desktop/R_Folder"
setwd(path)
AllData01<-data.frame(date=seq(as.Date(FirstDate),as.Date(LastDate),by="days"),dummy=1)
AllData02<-AllData01
TSeriesDataFileList<-dir(path)
for(iii in 1:length(TSeriesDataFileList))
{
 dataset<-read.table(file=TSeriesDataFileList[iii],header=TRUE,sep=",")
 dataset[,1]<-as.Date(dataset[,1])
 for(ccc in 2:ncol(dataset))
 {
  #以下は非数値データ行の削除及びデータの実数化。オリジナルデータに合わせて適宜変更。
  dataset<-subset(dataset,dataset[,ccc]!=".")
  dataset<-subset(dataset,is.na(dataset[,ccc])==F)
  dataset<-subset(dataset,as.Date(FirstDate)<=dataset[,1])
  dataset<-subset(dataset,as.Date(LastDate)>=dataset[,1])
  dataset[,ccc]<-as.double(as.character(dataset[,ccc]))
  #日付列名の統一
  colnames(dataset)[1]<-"date"
  #単位根検定 H0:単位根有り
  p0<-adf.test(dataset[,ccc],k=1)$p.value	
  p1<-adf.test(diff(dataset[,ccc]),k=1)$p.value	
  #出力
  cat(TSeriesDataFileList[iii],",",colnames(dataset)[ccc],"\n",sep="")
  cat("  1)単位根検定","\n",sep="")
  cat("    ・原系列,p値=",p0,"\n",sep="")
  cat("    ・一次階差,p値=",p1,"\n",sep="")
  cat("  2)Summary","\n",sep="")
  cat("    ・原系列","\n",sep="")
  print(summary(dataset[,ccc]))
  cat("    ・一次階差","\n",sep="")
  print(summary(diff(dataset[,ccc])))
  cat("  3)自己相関","\n",sep="")
  cat("    ・原系列",sep="")
  print(acf(dataset[,ccc],plot=F))
  cat("    ・一次階差",sep="")
  print(acf(diff(dataset[,ccc]),plot=F))
  cat("  4)ARIMA","\n",sep="")
  cat("    ・原系列","\n",sep="")
  for(d in 0:1){
    for(p in 0:2){
      for(q in 0:2){
        tmp <- arima(dataset[,ccc],order=c(p,d,q),method="ML")
        cat("     order=(",p,",",d,",",q,"),AIC=",tmp$aic,"\n")
      }
    }
  }
  cat("\n")
 }
 #print(head(dataset,3))
 cat("\n","\n")
 AllData01<-merge(AllData01,dataset,by="date",all=T,sort=T) #欠損はNAとして処理
 AllData02<-merge(AllData02,dataset,by="date",sort=T) #全系列に数値データが存在する日付のみ
}
#TSeriesDataFileList
#head(AllData01,10)
#head(AllData02,10)