I'm buliding keras model for mulitvariate time series classification. Data i'm using are separate in data frames by dimentions, so if there is an 2-variate time series I have 2 data frames each with the same number of columns (time stamps) and rows (observations). In this case my model should take the input (nrow, ncol, 2), but I have a problem with covering two data frames into one array of this dimentionality. An example data can be found here: https://timeseriesclassification.com/description.php?Dataset=Libras
I know it's a basic question but I can't find any easy way to do this.
Example:
dim1 <- data.frame(t1 = c(1:4), t2 = c(2:5), t3 = c(3:6))
dim2 <- data.frame(t1 = c(3:6), t2 = c(4:7), t3 = c(5:8))
So expected result should return:
dim(result)
> 4 3 2
And be an array.
We could use abind function:
?abind Combine multi-dimensional arrays. This is a generalization of cbind and rbind. Takes a sequence of vectors, matrices, or arrays and produces a single array of the same or higher dimension.
library(abind)
# Combine data frames into a single array
result <- abind(dim1, dim2, along = 3)
dim(result)
[1] 4 3 2