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result-analysis-ss.r
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result-analysis-ss.r
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load("final_results_samplesize_rbr.rdata")
load("stationarity_tsdl.rdata")
load("series_length.rdata")
# err_estimation <-
# lapply(final_results[!(is_stationary_2ensemble[len>1000])],
# function(x) {
# x <- do.call(rbind, x)
# rnk <- t(apply(abs(x),1,rank))
# rnk
# })
err_estimation <-
lapply(final_results,
function(x) {
x <- do.call(rbind, x)
rnk <- t(apply(abs(x),1,rank))
rnk
})
err_arr <- simplify2array(err_estimation)
err_ovr <- apply(err_arr,1:2,mean)
library(tsensembler)
err_ovr <-
roll_mean_matrix(as.data.frame(err_ovr), 2)
colnames(err_ovr) <-
c("CV", "CV-Bl", "CV-Mod","CV-hvBl",
"Preq-Bls", "Preq-Sld-Bls",
"Preq-Bls-Gap","Holdout", "Rep-Holdout",
"Preq-Slide","Preq-Grow")
df <- t(round(err_ovr,2))
df <- as.data.frame(df)
df$Method <- as.factor(rownames(df))
rownames(df) <- NULL
colnames(df) <- c(as.character(seq(from=100,to=900,by=100)),"Method")
library(hrbrthemes)
library(GGally)
library(viridis)
ggparcoord(df,
columns = 1:9, groupColumn = 10,
scale = "globalminmax",
showPoints = TRUE,
alphaLines = 0.9
) + theme_minimal() +
labs(x="Training Sample Size",
y= "Average Rank")