滞后时间序列数据 [英] Lagging time series data

问题描述

我一直在寻找一种用于预测的神经网络模型.我正在尝试使数据具有图片中显示的格式，以便模型可以根据给定的先前值(2天前，1天前，今天)进行预测.这些值将在第二天针对下一个预测进行调整，例如如图中所示，从第一个输入开始的1天前变为在第二个天的2天前.我使用滞后函数滞后时间序列的数据，并使用矩阵以这种格式设置数据，但是我对此感到非常困惑和困惑.如何使用滞后函数将图像中所示的数据滞后?

I was looking to build a Neural network model for prediction. I am trying to get my data to be in the format that is shown in the image so the model can predict given the previous values of (2 days ago, 1 day ago, today). These values will be adjusted the next day for the next prediction e.g. 1 day ago from first input becomes 2 days ago in the second as seen in the image. I am using the lag function to lag the data for time series and a matrix to set up the data in this format but I am quite confused and struggling with this for sometime. How could I lag the data as shown in the image using the lag function?

我当前的代码:

data <-
  structure(
    list(
      `USD/EUR` = c(
        1.373,
        1.386,
        1.3768,
        1.3718,
        1.3774,
        1.3672,
        1.3872,
        1.3932,
        1.3911,
        1.3838,
        1.4171,
        1.4164,
        1.3947,
        1.3675,
        1.3801,
        1.3744,
        1.3759,
        1.3743,
        1.3787,
        1.3595,
        1.3599,
        1.3624,
        1.3523,
        1.3506,
        1.3521
      )
    ),
    row.names = c(NA,-25L),
    class = c("tbl_df",
              "tbl", "data.frame")
  )

#Lag the data
lagData <- c(lag(data$`USD/EUR`,k = 1))
lagData

#store data into matrix to feed to neural net
matrixForm <- matrix(lagData, nrow = 25, ncol = 4, byrow = TRUE)
matrixForm

推荐答案

1)使用嵌入.不使用任何软件包.

1) Use embed. No packages are used.

embed(data[[1]], 4)[, 4:1]

给出此矩阵:

        [,1]   [,2]   [,3]   [,4]
 [1,] 1.3730 1.3860 1.3768 1.3718
 [2,] 1.3860 1.3768 1.3718 1.3774
 [3,] 1.3768 1.3718 1.3774 1.3672
 [4,] 1.3718 1.3774 1.3672 1.3872
 ...snip...

2)另一种可能性是折叠包中的标志(快速滞后):

2) Another possibility is flag (fast lag) in the collapse package:

na_omit(flag(data[[1]], 3:0))

给予:

          L3     L2     L1     --
 [1,] 1.3730 1.3860 1.3768 1.3718
 [2,] 1.3860 1.3768 1.3718 1.3774
 [3,] 1.3768 1.3718 1.3774 1.3672
 [4,] 1.3718 1.3774 1.3672 1.3872
 ...snip...

3)动物园以与标记类似的方式支持多个延迟，但它使用与R中相同的方向.确保dplyr未加载 ，因为它会覆盖延迟

3) zoo supports multiple lags in a similar manner to flag except it uses the same orientation as in R. Be sure that dplyr is not loaded since it overwrites lag.

library(zoo)

na.omit(lag(zoo(data[[1]]), -3:0))

给予该动物园对象:

    lag-3  lag-2  lag-1   lag0
4  1.3730 1.3860 1.3768 1.3718
5  1.3860 1.3768 1.3718 1.3774
6  1.3768 1.3718 1.3774 1.3672
7  1.3718 1.3774 1.3672 1.3872
...snip...

这篇关于滞后时间序列数据的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！