滾動窗口

Question

之後創建預測矩陣I具有的數據集是這樣的：

data <- as.zoo(ts.union(a=arima.sim(model=list(ar=c(.9,-.2)), n=144), 
         b=arima.sim(model=list(ar=c(.6, -.3)), n=144), 
         c=arima.sim(model=list(ar=c(-.2,-.6)), n=144))) 

我製成的滾動窗口預測a，它提供的6步驟的每個步驟：

rolling.window <- rollapply(data, width = 132, 
          FUN = function(x) predict(VAR(x, type="const", ic="FPE"), 
                n.ahead=6, ci=0.95)$fcst$a[,1], 
         by.column = F, align = "right") 

head(rolling.window) 

132 0.086474 0.031416 0.00071186 -0.016284 -0.025615 -0.030692 
133 1.289223 0.762734 0.46166288 0.284157 0.180816 0.120837 
134 0.307354 0.332732 0.28306490 0.223481 0.171789 0.132596 
135 0.105074 0.148357 0.14704495 0.128852 0.109577 0.093722 
136 -0.469992 -0.496095 -0.39268676 -0.263921 -0.155009 -0.074600 
137 -1.047158 -0.720692 -0.45201041 -0.251064 -0.115632 -0.029640 

提前預測

現在，我想自動保存這些預測在矩陣（或多個時間序列對象）這樣的：

 w132  w133 w134  w135  w136  w137 
133 0.08647370 NA  NA  NA  NA  NA 
134 0.03141553 1.28922 NA  NA  NA  NA 
135 0.00071186 0.76273 0.30735 NA  NA  NA 
136 -0.01628371 0,46166 0.33273 0.105074 NA  NA 
137 -0.02561482 0.28416 0.28306 0.148357 -0.46999 NA 
138 -0.03069235 0.18082 0.22348 0.147045 -0.49610 -1.04716 
139  NA  0.12084 0.17179 0.128852 -0.39269 -0.72069 
140  NA  NA 0.13260 0.109577 -0.26392 -0.45201 
141  NA  NA  NA 0.093722 -0.15501 -0.25106 
142  NA  NA  NA  NA -0.07460 -0.11563 
143  NA  NA  NA  NA  NA -0.02964 

等等。我希望每個滾動窗口都能在相應的時間提前6步預測。不幸的是，我完全不知道我應該從哪裏開始。我嘗試了lag()，但這隻適用於一個系列。我也無法解決，我如何在rollapply()函數中做到這一點。你能給我一個提示嗎？

Answer 1

你可以這樣做：

do.call(cbind, lapply(1:nrow(df), function(i) c(rep(NA,i-1), df[i,], rep(NA, nrow(df)-i)))) 

    [,1]  [,2]  [,3]  [,4]  [,5]  [,6]  
V2 0.086474 NA  NA  NA  NA   NA   
V3 0.031416 1.289223 NA  NA  NA   NA   
V4 0.00071186 0.762734 0.307354 NA  NA   NA   
V5 -0.016284 0.4616629 0.332732 0.105074 NA   NA   
V6 -0.025615 0.284157 0.2830649 0.148357 -0.469992 NA   
V7 -0.030692 0.180816 0.223481 0.147045 -0.496095 -1.047158 
    NA   0.120837 0.171789 0.128852 -0.3926868 -0.720692 
    NA   NA  0.132596 0.109577 -0.263921 -0.4520104 
    NA   NA  NA  0.093722 -0.155009 -0.251064 
    NA   NA  NA  NA  -0.0746 -0.115632 
    NA   NA  NA  NA  NA   -0.02964 

數據：

df = structure(list(V2 = c(0.086474, 1.289223, 0.307354, 0.105074, 
-0.469992, -1.047158), V3 = c(0.031416, 0.762734, 0.332732, 0.148357, 
-0.496095, -0.720692), V4 = c(0.00071186, 0.46166288, 0.2830649, 
0.14704495, -0.39268676, -0.45201041), V5 = c(-0.016284, 0.284157, 
0.223481, 0.128852, -0.263921, -0.251064), V6 = c(-0.025615, 
0.180816, 0.171789, 0.109577, -0.155009, -0.115632), V7 = c(-0.030692, 
0.120837, 0.132596, 0.093722, -0.0746, -0.02964)), .Names = c("V2", 
"V3", "V4", "V5", "V6", "V7"), row.names = 132:137, class = "data.frame")