---
title: "WASP: An R package for Wavelet System Prediction"
author: 'Ze Jiang, Md. Mamunur Rashid, Ashish Sharma, and Fiona Johnson'
date: "`r format(Sys.time(), '%H:%M:%S %d %B, %Y')`"
output: 
#  rmarkdown::html_vignette: 
  bookdown::html_vignette2: 
    toc: true
    number_sections: true
vignette: >
  %\VignetteIndexEntry{Wavelet System Prediction}
  %\VignetteEncoding{UTF-8}
  %\VignetteEngine{knitr::rmarkdown}
editor_options: 
  chunk_output_type: console
---
# Setup
```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>", warning = FALSE, 
  
  out.width = "85%",
  fig.align = "center", fig.pos = "h!"
)
options(rmarkdown.html_vignette.check_title = FALSE)
library(rmarkdown)
library(knitr)
library(kableExtra)
```

# Required packages
```{r packages}
library(WASP)
library(ggplot2)

if(!require(SPEI)) devtools::install_github('sbegueria/SPEI@v1.7.1') # use 1.7.1
require(SPEI)
library(readr)
library(dplyr)
library(FNN)
library(synthesis)
library(waveslim)
library(cowplot)
library(gridGraphics)
```

# DWT, MODWT and AT basic propertites

```{r wavelet-transforms}
# data generation
x <- arima.sim(list(order = c(1, 0, 0), ar = 0.6), n = 512)
# x <- as.numeric(scale(data.gen.Rossler(time = seq(0, 50, length.out = 512))$x, scale=F))

# Daubechies wavelets
for (wf in c("haar", "d4", "d8", "d16")) {
  print(paste0("Wavelet filter: ", wf))
  #----------------------------------------------------------------------------
  # wavelet family, extension mode and package
  # wf <- "haar" # wavelet family D8 or db4
  boundary <- "periodic"
  if (wf != "haar") v <- as.integer(parse_number(wf) / 2) else v <- 1

  # Maximum decomposition level J
  n <- length(x)
  J <- ceiling(log(n / (2 * v - 1)) / log(2)) - 1 # (Kaiser, 1994)

  cov <- rnorm(J + 1, sd = 2)
  Vr <- as.numeric(cov / norm(cov, type = "2") * sd(x))
  #----------------------------------------------------------------------------
  # DWT-MRA
  print("-----------DWT-MRA-----------")
  x.mra <- waveslim::mra(x, wf = wf, J = J, method = "dwt", boundary = boundary)
  x.mra.m <- matrix(unlist(x.mra), ncol = J + 1)

  x.n <- scale(x.mra.m) %*% Vr
  var(x.n) - var(x)

  message(paste0("Additive decompostion: ", isTRUE(all.equal(as.numeric(x), rowSums(x.mra.m)))))
  message(paste0("Variance decompostion: ", isTRUE(all.equal(var(x), sum(apply(x.mra.m, 2, var))))))

  #----------------------------------------------------------------------------
  # MODWT
  print("-----------MODWT-----------")
  x.modwt <- waveslim::modwt(x, wf = wf, n.levels = J, boundary = boundary)
  x.modwt.m <- matrix(unlist(x.modwt), ncol = J + 1)

  x.n <- scale(x.modwt.m) %*% Vr
  var(x.n) - var(x)

  message(paste0("Additive decompostion: ", isTRUE(all.equal(as.numeric(x), rowSums(x.modwt.m)))))
  message(paste0("Variance decompostion: ", isTRUE(all.equal(var(x), sum(apply(x.modwt.m, 2, var))))))

  #----------------------------------------------------------------------------
  # a trous
  print("-----------AT-----------")
  x.at <- at.wd(x, wf = wf, J = J, boundary = boundary)
  x.at.m <- matrix(unlist(x.at), ncol = J + 1)

  # x.mra.modwt <- waveslim::mra(x,wf=wf, J=J, method="modwt", boundary=boundary)
  # x.mra.modwt <- matrix(unlist(x.mra.modwt), ncol=J+1)
  #
  # print(sum(abs(x.at.m-x.mra.modwt)))

  message(paste0("Additive decompostion: ", isTRUE(all.equal(as.numeric(x), rowSums(x.at.m)))))
  message(paste0("Variance decompostion: ", isTRUE(all.equal(var(x), sum(apply(x.at.m, 2, var))))))

  if (isTRUE(all.equal(x.at.m, x.modwt.m))) {
    message(paste0("AT and MODWT is equivalent using the", wf, "!"))
  }
}
```

## Summary of various properties for the three DWT methods

```{r tab1}
tab1 <- data.frame(
  col1 = c("DWT-MRA", "MODWT", "AT"),
  col2 = c("$\\checkmark$", "", "$\\checkmark$"),
  col3 = c("$\\checkmark$", "$\\checkmark$", ""),
  col4 = c("", "$\\checkmark$", "$\\checkmark$"),
  col5 = c("$\\checkmark$", "", "")
)

colnames(tab1) <- c("Wavelet Method", "Additive decomposition", "Variance decomposition", "No dependence on future data", "Dyadic sample size")

kable(tab1, caption = "Summary of various properties for the three DWT methods", booktabs = T, escape = F) %>%
  kable_styling(latex_options = c("HOLD_position"), position = "center") %>%
  column_spec(1, width = "6em") %>%
  column_spec(2:5, width = "7em") %>%
  footnote(general = "When Haar wavelet filter is used, MODWT and AT are equivalent and both of them preserves additive and variance decomposition.", footnote_as_chunk = T)
```

## Illustration of three types of DWT methods 

```{r wavelet-decomposition, fig.show='hide'}
p.list <- NULL
wf.opts <- c("d16", "haar")
for (k in seq_along(wf.opts)) {
  # data generation
  x <- arima.sim(list(order = c(1, 0, 0), ar = 0.6), n = 128)

  #----------------------------------------------------------------------------
  # wavelet family, extension mode and package
  wf <- wf.opts[k] # wavelet family D8 or db4
  boundary <- "periodic"
  if (wf != "haar") v <- as.integer(parse_number(wf) / 2) else v <- 1

  # Maximum decomposition level J
  n <- length(x)
  J <- ceiling(log(n / (2 * v - 1)) / log(2)) - 1 # (Kaiser, 1994)

  limits.x <- c(0, n)
  limits.y <- c(-3, 3)
  #----------------------------------------------------------------------------
  # DWT-MRA
  x.mra <- waveslim::mra(x, wf = wf, J = J, method = "dwt", boundary = boundary)
  x.mra.m <- matrix(unlist(x.mra), ncol = J + 1)

  p1 <- mra.plot(x, x.mra.m, limits.x, limits.y,
    ylab = "X", col = "red", type = "details",
    main = paste0("DWT-MRA", "(", wf, ")"), ps = 12
  )
  # p1 <- recordPlot()

  #----------------------------------------------------------------------------
  # MODWT
  x.modwt <- waveslim::modwt(x, wf = wf, n.levels = J, boundary = boundary)
  x.modwt.m <- matrix(unlist(x.modwt), ncol = J + 1)

  p2 <- mra.plot(x, x.modwt.m, limits.x, limits.y,
    ylab = "X", col = "red", type = "coefs",
    main = paste0("MODWT", "(", wf, ")"), ps = 12
  )

  #----------------------------------------------------------------------------
  # a trous
  x.at <- at.wd(x, wf = wf, J = J, boundary = boundary)
  x.at.m <- matrix(unlist(x.at), ncol = J + 1)

  p3 <- mra.plot(x, x.at.m, limits.x, limits.y,
    ylab = "X", col = "red", type = "coefs",
    main = paste0("AT", "(", wf, ")"), ps = 12
  )

  p.list[[k]] <- list(p1, p2, p3)
}
```

###  Daubechies 16 wavelet

```{r figa, fig.cap='Illustration of three types of DWT methods', fig.width=9, fig.height=7}
#----------------------------------------------------------------------------
# plot and save
cowplot::plot_grid(
  plotlist = p.list[[1]], ncol = 3, labels = c("(a)", "(b)", "(c)"),
  label_size = 12
)
```

### Haar wavelet filter

```{r figb, fig.cap='Illustration of three types of DWT methods', fig.width=9, fig.height=7}
#----------------------------------------------------------------------------
# plot and save
cowplot::plot_grid(
  plotlist = p.list[[2]], ncol = 3, labels = c("(a)", "(b)", "(c)"),
  label_size = 12
)
```

## Wavelet transform: decompostion level 

```{r wt-decomposition-level}
sample <- seq(100, by = 200, length.out = 5)
v <- 2 # vanishing moment
tmp <- NULL
for (n in sample) {
  J1 <- floor(log(n / (2 * v - 1)) / log(2))
  J # (Kaiser, 1994)
  J2 <- floor(log2(n / (2 * v - 1) - 1))
  J # Cornish, C. R., Bretherton, C. S., & Percival, D. B. (2006)
  J3 <- floor(log10(n))
  J # (Nourani et al., 2008)

  tmp <- cbind(tmp, c(J1, J2, J3))
}

tab <- tmp
colnames(tab) <- sample
rownames(tab) <- paste0("Method", 1:3)

kable(tab,
  caption = "Decompostion level with varying sample size",
  booktabs = T, align = "c", digits = 3
) %>%
  kable_styling("striped", position = "center", full_width = FALSE) # %>%
# collapse_rows(columns = 1:2, valign = "middle")
```

# Variance transformation

## Optimal preditive accuracy (RMSE)

```{r optimal-variance-transformation, warning=TRUE}
if (TRUE) {
  ### Synthetic example
  # data generation
  set.seed(2020)
  sample <- 512
  # frequency, sampled from a given range
  fd <- c(3, 5, 10, 15, 25, 30, 55, 70, 95)
  # data <- WASP::data.gen.SW(nobs=sample,fp=25,fd=fd)
  data <- WASP::data.gen.SW(nobs = sample, fp = c(15, 25, 30), fd = fd)

  # ts = data.gen.Rossler(time = seq(0, 50, length.out = sample))
  # data <- list(x=ts$z, dp=cbind(ts$x, ts$y))
} else {
  ### Real-world example
  data("obs.mon")
  data("rain.mon")

  if (TRUE) { # SPI12 as response
	#SPI.12 <- SPEI::spi(rain.mon[, 5], scale = 12)$fitted
	SPI.12 <- SPI.calc(window(rain.mon[, 5], start=c(1949,1), end=c(2009,12)),sc=12)
    x <- window(SPI.12, start = c(1950, 1), end = c(2009, 12))
    dp <- window(obs.mon, start = c(1950, 1), end = c(2009, 12))
  } else { # rainfall as response
    x <- window(rain.mon[, 5], start = c(1950, 1), end = c(2009, 12))
    dp <- window(obs.mon, start = c(1950, 1), end = c(2009, 12))
  }
  data <- list(x = x, dp = dp)
  sample <- length(x)
}

# plot.ts(cbind(data$x,data$dp))

tab.list <- list()
mode.opts <- c("MRA", "MODWT", "AT")
for (mode in mode.opts) {
  print(mode)

  # cov.opt <- switch(2,"auto","pos","neg")
  if (mode == "MRA") {
    method <- switch(1,
      "dwt",
      "modwt"
    )
  }

  # wavelet family, extension mode and package
  # wf <- switch(mode, "MRA"="haar", "MODWT"="haar", "AT"="haar")
  wf <- "haar"
  pad <- "zero"
  boundary <- "periodic"
  if (wf != "haar") v <- as.integer(parse_number(wf) / 2) else v <- 1

  # Maximum decomposition level J
  n <- sample
  J <- ceiling(log(n / (2 * v - 1)) / log(2)) - 1 # (Kaiser, 1994)

  tab <- NULL
  for (cov.opt in c("auto", "pos", "neg")) {
    # variance transform - calibration
    if (mode == "MRA") {
      dwt <- dwt.vt(data, wf, J, method, pad, boundary, cov.opt)
    } else if (mode == "MODWT") {
      dwt <- modwt.vt(data, wf, J, boundary, cov.opt)
    } else {
      dwt <- at.vt(data, wf, J, boundary, cov.opt)
    }

    # optimal prediction accuracy
    opti.rmse <- NULL
    dp.RMSE <- NULL
    dp.n.RMSE <- NULL
    S <- dwt$S
    ndim <- ncol(S)
    for (i in 1:ndim) {
      x <- dwt$x
      dp <- dwt$dp[, i]
      dp.n <- dwt$dp.n[, i]

      # ts.plot(cbind(dp,dp.n), col=1:2)

      dp.RMSE <- c(dp.RMSE, sqrt(mean(lm(x ~ dp)$residuals^2)))
      dp.n.RMSE <- c(dp.n.RMSE, sqrt(mean(lm(x ~ dp.n)$residuals^2)))

      # small difference due to the reconstruction
      opti.rmse <- c(opti.rmse, sqrt((n - 1) / n * (var(x) - sum(S[, i]^2) * var(dp) / var(dp.n))))
      # opti.rmse <- c(opti.rmse, sqrt((n-1)/n*(var(x)-sum(S[,i]^2))))
    }

    tab <- rbind(tab, data.frame(cov.opt, var=1:ndim, dp.RMSE, dp.n.RMSE, opti.rmse))
  }

  colnames(tab) <- c("Sign of covariance", "Variable", "Std", "VT", "Optimal")
  tab.list[[length(tab.list) + 1]] <- tab
}

# print(tab.list)

kable(tab.list[[1]], caption = "Optimal RMSE using DWT-based VT",
      booktabs = T, align = "c", digits = 3) %>%
kable_styling("striped", position = "center", full_width = FALSE)  %>%
collapse_rows(columns = 1, valign = "middle")
```

```{r optimal-variance-transformation1, warning=TRUE}
kable(tab.list[[2]], caption = "Optimal RMSE using MODWT/AT-based VT",
      booktabs = T, align = "c", digits = 3) %>%
kable_styling("striped", position = "center", full_width = FALSE)  %>%
collapse_rows(columns = 1, valign = "middle")
```

## Transformed predictor variables

```{r variance-transform, fig.keep='last', fig.cap='Orignal and VT predictors. (a): DWT-MRA (b): MODWT/AT', fig.width=9, fig.height=6}
#-------------------------------------------------------------------
if (TRUE) {
  set.seed(2020)
  ### synthetic example - Rossler
  sample <- 10000
  s <- 0.1
  ts.list <- list()
  for (i in seq_along(s)) {
    ts.r <- data.gen.Rossler(a = 0.2, b = 0.2, w = 5.7, start = c(-2, -10, 0.2), time = seq(0, 50, length.out = sample))

    # add noise
    ts.r$x <- ts(ts.r$x + rnorm(n = sample, mean = 0, sd = s[i]))
    ts.r$y <- ts(ts.r$y + rnorm(n = sample, mean = 0, sd = s[i]))
    ts.r$z <- ts(ts.r$z + rnorm(n = sample, mean = 0, sd = s[i]))

    ts.list[[i]] <- ts.r
  }

  data.list <- lapply(ts.list, function(ts) list(x = ts$z, dp = cbind(ts$x, ts$y)))

  lab.names <- c("x", "y")
  xlim<- c(0,n);ylim <- c(-55, 55)
} else {

  ### Real-world example
  data("obs.mon")
  data("rain.mon")

  #SPI.12 <- SPEI::spi(rain.mon[, 5], scale = 12)$fitted
  SPI.12 <- SPI.calc(window(rain.mon[, 5], start=c(1949,1), end=c(2009,12)),sc=12)
  x <- window(SPI.12, start = c(1950, 1), end = c(2009, 12))
  dp <- window(obs.mon, start = c(1950, 1), end = c(2009, 12))

  data.list <- list(list(x = x, dp = dp))
  sample <- length(x)

  lab.names <- colnames(obs.mon)
  xlim<- NULL;ylim <- NULL
}

#-------------------------------------------------------------------
p.list <- list()
dp.list <- list()
if (wf != "haar") mode.opts <- c("MRA", "MODWT", "AT")[1:3] else mode.opts <- c("MRA", "MODWT","AT")[1:2]
for (mode in mode.opts) {
  cov.opt <- switch(1,
    "auto",
    "pos",
    "neg"
  )
  flag <- switch(1,
    "biased",
    "unbiased"
  )
  if (mode == "MRA") {
    method <- switch(1,
      "dwt",
      "modwt"
    )
  }

  # wavelet family, extension mode and package
  # wf <- switch(mode, "MRA"="haar", "MODWT"="haar", "AT"="haar")
  wf <- "d16"
  pad <- "zero"
  boundary <- "periodic"
  if (wf != "haar") v <- as.integer(parse_number(wf) / 2) else v <- 1

  # Maximum decomposition level J
  n <- sample
  J <- ceiling(log(n / (2 * v - 1)) / log(2)) - 1 # (Kaiser, 1994)
  # J <- floor(log(n/(2*v-1))/log(2))

  # variance transform - calibration
  if (mode == "MRA") {
    dwt.list <- lapply(data.list, function(x) dwt.vt(x, wf, J, method, pad, boundary, cov.opt, flag))
  } else if (mode == "MODWT") {
    dwt.list <- lapply(data.list, function(x) modwt.vt(x, wf, J, boundary, cov.opt, flag))
  } else {
    dwt.list <- lapply(data.list, function(x) at.vt(x, wf, J, boundary, cov.opt, flag))
  }

  for (j in 1:length(dwt.list)) {
    dwt <- dwt.list[[j]]

    par(
      mfrow = c(ncol(dwt$dp), 1), mar = c(0, 2.5, 2, 1),
      oma = c(2, 1, 0, 0), # move plot to the right and up
      mgp = c(1.5, 0.5, 0), # move axis labels closer to axis
      pty = "m", bg = "transparent",
      ps = 12
    )

    # plot(dwt$x, type="l", xlab=NA, ylab="SPI12", col="red")
    # plot(dwt$x, type="l", xlab=NA, ylab="Rain", col="red")
    for (i in 1:ncol(dwt$dp)) {
      ts.plot(cbind(dwt$dp[, i], dwt$dp.n[, i]),
        xlab = NA, ylab = paste0(lab.names[i]),
        xlim = xlim, ylim = ylim,
        col = c("black", "blue"), lwd = c(1, 2)
      )
    }

    p.list[[length(p.list) + 1]] <- recordPlot()

    dp.list[[length(dp.list) + 1]] <- dwt$dp.n
  }
}

#----------------------------------------------------------------------------
# plot and save
fig <- cowplot::plot_grid(plotlist = p.list, nrow = 1, labels = c("(a)", "(b)", "(c)"))
fig
```

# Stepwise variance transformation

```{r svt, fig.keep='last', fig.cap='Orignal and SVT predictors. (a): DWT-MRA (b): MODWT/AT', fig.width=9, fig.height=6}
#-------------------------------------------------------------------
### Real-world example
data("obs.mon")
data("rain.mon")
op <- par()
station.id <- 5
lab.names <- colnames(obs.mon)[c(1, 3, 4, 5, 7)]

if (TRUE) { # SPI12 as response
  #SPI.12 <- SPEI::spi(rain.mon, scale = 12)$fitted
  SPI.12 <- SPI.calc(window(rain.mon, start=c(1949,1), end=c(2009,12)),sc=12)
  x <- window(SPI.12, start = c(1950, 1), end = c(2009, 12))
  dp <- window(obs.mon[, lab.names], start = c(1950, 1), end = c(2009, 12))
} else { # rainfall as response
  x <- window(rain.mon, start = c(1950, 1), end = c(2009, 12))
  dp <- window(obs.mon[, lab.names], start = c(1950, 1), end = c(2009, 12))
}

data.list <- lapply(station.id, function(id) list(x = x[, id], dp = dp))


ylim <- data.frame(
  GPH = c(700, 900), TDP700 = c(5, 25), TDP500 = c(5, 25), EPT = c(300, 330),
  UWND = c(-5, 25), VWND = c(-5, 10), MSLP = c(-1, 1)
)[c(1, 3, 4, 5, 7)]

#-------------------------------------------------------------------
p.list <- list()
RMSE <- NULL
mode.opts <- c("MRA", "MODWT", "AT")[1:2]
for (mode in mode.opts) {
  cov.opt <- switch(1,
    "auto",
    "pos",
    "neg"
  )
  if (mode == "MRA") {
    method <- switch(1,
      "dwt",
      "modwt"
    )
  }

  # wavelet family, extension mode and package
  wf <- switch(mode,
    "MRA" = "d4",
    "MODWT" = "haar",
    "AT" = "haar"
  )
  pad <- "zero"
  boundary <- "periodic"
  if (wf != "haar") v <- as.integer(parse_number(wf) / 2) else v <- 1

  # Maximum decomposition level J
  n <- nrow(x)
  J <- ceiling(log(n / (2 * v - 1)) / log(2)) - 1 # (Kaiser, 1994)

  # high order variance transformation
  dwt.list <- lapply(data.list, function(data) stepwise.VT(data, mode = mode, wf = wf, J=J))

  for (j in seq_len(length(dwt.list))) {
    dwt <- dwt.list[[j]]
    cpy <- dwt$cpy

    MSE <- NULL
    for (i in seq_len(length(cpy))) {
      m1 <- sqrt(FNN::knn.reg(train = dwt$dp[, 1:i], y = dwt$x)$PRESS / n)
      m2 <- sqrt(FNN::knn.reg(train = dwt$dp.n[, 1:i], y = dwt$x)$PRESS / n)

      MSE <- rbind(MSE, c(m1, m2))
    }

    RMSE <- rbind(RMSE, data.frame(mode, MSE))

    par(
      mfrow = c(length(cpy), 1), mar = c(0, 4, 2, 1),
      oma = c(2, 1, 0, 0), # move plot to the right and up
      mgp = c(1.5, 0.5, 0), # move axis labels closer to axis
      pty = "m", bg = "transparent",
      ps = 8
    )

    # plot(dwt$x, type="l", xlab=NA, ylab="SPI12", ylim=c(-3,3),col="red")
    # plot(dwt$x, type="l", xlab=NA, ylab="Rain", col="red")
    for (i in seq_len(length(cpy))) {
      ts.plot(dwt$dp[, i], dwt$dp.n[, i],
        xlab = NA, ylab = paste0(lab.names[cpy[i]]), # ylim=ylim[,i],
        col = c("black", "blue"), lwd = c(1, 2)
      )
    }

    p.list[[length(p.list) + 1]] <- recordPlot()
  }
}
par(op)
#-------------------------------------------------------------------
# plot and save
cowplot::plot_grid(plotlist = p.list, nrow = 1, labels = c("(a)", "(b)", "(c)"))

#-------------------------------------------------------------------
# RMSE when more predictors are included
#tab1 <- round(RMSE, 3)
#tab1 <- cbind(1:nrow(tab1), tab1)
#colnames(tab1) <- c("No. of Predictors", rep(c("Original", "Transformed"), length(mode.opts)))
# kable(tab1, caption = "Comparison of prediction accuracy using Std and SVT", booktabs = T) %>%
#   kable_styling(latex_options = c("HOLD_position"), position = "center", full_width = FALSE)  %>%
#   #  add_header_above(c(" " = 1, "DWT-MRA" = 2, "MODWT" = 2, "AT" = 2))
#   add_header_above(c(" " = 1, "DWT-MRA" = 2, "MODWT/AT" = 2))
tab1 <- RMSE %>% group_by(mode) %>% mutate(id = row_number())
colnames(tab1) <- c("Method","No. of Predictors","Original","Transformed")
kable(tab1[,c(1,4,2,3)], caption = "Comparison of prediction accuracy using Std and SVT", booktabs = T, 
      digits = 3) %>%
  kable_styling(latex_options = c("HOLD_position"), position = "center", full_width = FALSE)  %>%
  collapse_rows(columns = 1)

```

<!-- # Comparison with traditional wavelet-based methods -->

```{r comp, eval=FALSE, include=FALSE}
#-------------------------------------------------------------------
sample <-  100000
sample.cal <- sample/2
k <- ceiling(sqrt(sample/2))

s=0.1
#s=c(0.1,0.5,1) # scaling factor for noise level
set.seed(2020)

###synthetic example - Rossler
ts.list <- list()
for(i in seq_along(s)){
  ts.r <- data.gen.Rossler(a = 0.2, b = 0.2, w = 5.7, start = c(-2, -10, 0.2), time = seq(0, 50, length.out = sample))
  
  #add noise
  ts.r$x <- ts(ts.r$x + rnorm(n = sample, mean=0, sd=s[i]))
  ts.r$y <- ts(ts.r$y + rnorm(n = sample, mean=0, sd=s[i]))
  ts.r$z <- ts(ts.r$z + rnorm(n = sample, mean=0, sd=s[i]))
  
  ts.list[[i]]<- ts.r
}

#-------------------------------------------------------------------
tab3<-NULL
mode.opts <- c("MRA", "MODWT","a trous")[1:2]
for(mode in mode.opts){
  ### wavelet method selection
  #mode <- switch(3,"MRA", "MODWT","a trous")
  cov.opt <- switch(1,"auto","pos","neg")
  if(mode=="MRA") method <- switch(1,"dwt","modwt")

  # wavelet family, extension mode and package
  wf <- "haar" # wavelet family D8 or db4
  pad <-  "zero"
  boundary <- "periodic"
  if(wf!="haar") v <- as.integer(as.numeric(substr(wf,2,3))/2) else v <- 1
  

  ###proposed method----------------------------------------------------------
  #--------------------------------------------------
  #calibration dataset
  data.list <- lapply(ts.list, function(ts) list(x=ts$z[1:sample.cal], dp=cbind(ts$x[1:sample.cal],ts$y[1:sample.cal])))
  
  n <- sample.cal
  J <- ceiling(log(n/(2*v-1))/log(2)) - 1
  #if(wf=="haar"&&mode=="MODWT") J = J-1 #since modwt no need a dyadic number size
  print(paste0("Calibration: Decomposition Levels J= ",J))
  
  #variance transform
  if(mode=="MRA"){
    dwt.list<- lapply(data.list, function(x) dwt.vt(x, wf, J, method, pad, boundary, cov.opt))
  } else if(mode=="MODWT") {
    dwt.list<- lapply(data.list, function(x) modwt.vt(x, wf, J, boundary, cov.opt))
  } else {
    dwt.list<- lapply(data.list, function(x) at.vt(x, wf, J, boundary, cov.opt))
  }
  
  #--------------------------------------------------
  # calibration
  df <- NULL;data.RMSE<-NULL;dwt.RMSE<-NULL
  sd.cal<-NULL; cor.cal<-NULL
  for(i in 1:length(dwt.list)){
  
    dwt <- dwt.list[[i]]
    dp <- dwt$dp; dp.n <- dwt$dp.n; x <- dwt$x
  
    m1 <- FNN::knn.reg(dp, y=x, k=k)$pred
    m2 <- FNN::knn.reg(dp.n, y=x, k=k)$pred
  
    data.RMSE <-c(data.RMSE, round(sqrt(mean((x-m1)^2)),3))
    dwt.RMSE <- c(dwt.RMSE, round(sqrt(mean((x-m2)^2)),3))
  
    sd.cal <- cbind(sd.cal, as.vector(c(sd(x),sd(m1),sd(m2))))
    cor.cal <-cbind(cor.cal, cor(cbind(x,m1,m2))[,1])
  
    df1 <- data.frame(Group=1, s=s[i], No=1:sample.cal,Pred=m1, Obs=x)
    df2 <- data.frame(Group=2, s=s[i], No=1:sample.cal,Pred=m2, Obs=x)
  
    df <- rbind(df, rbind(df1,df2))
  
  }
  #summary(df)
  #print(rbind(data.RMSE,dwt.RMSE))
  
  t1 <- rbind(data.RMSE,dwt.RMSE)
  sd.cal;cor.cal
  
  #--------------------------------------------------
  #validataion dataset
  data.list.val <- lapply(ts.list, function(ts) list(x=ts$z[(sample.cal+1):sample], dp=cbind(ts$x[(sample.cal+1):sample], ts$y[(sample.cal+1):sample])))
  
  sample.val <- sample-sample.cal
  n <- sample.val
  J <- ceiling(log(n/(2*v-1))/log(2)) - 1
  #if(wf=="haar"&&mode=="MODWT") J = J-1 #since modwt no need a dyadic number size
  print(paste0("Validation: Decomposition Levels J= ",J))
  
  #--------------------------------------------------
  #variance transform
  if(mode=="MRA"){
    dwt.list.val<- lapply(1:length(data.list.val), function(i) dwt.vt.val(data.list.val[[i]], J, dwt.list[[i]]))
  } else if(mode=="MODWT"){
    dwt.list.val<- lapply(1:length(data.list.val), function(i) modwt.vt.val(data.list.val[[i]], J, dwt.list[[i]]))
  } else {
    dwt.list.val<- lapply(1:length(data.list.val), function(i) at.vt.val(data.list.val[[i]], J, dwt.list[[i]]))
  }
  
  #--------------------------------------------------
  # validation
  df.val <- NULL;data.RMSE <-NULL;dwt.RMSE<-NULL
  sd.val<-NULL; cor.val<-NULL
  for(i in 1:length(dwt.list.val)){
  
    dwt <- dwt.list[[i]]
    dp <- dwt$dp; dp.n <- dwt$dp.n; x.train <- dwt$x
  
    dwt <- dwt.list.val[[i]]
    dp.v <- dwt$dp; dp.n.v <- dwt$dp.n; x <- dwt$x
  
    m1 <- FNN::knn.reg(train=dp, test=dp.v, y=x.train, k=k)$pred
    m2 <- FNN::knn.reg(train=dp.n, test=dp.n.v, y=x.train, k=k)$pred
  
    data.RMSE <-c(data.RMSE, round(sqrt(mean((m1-x)^2)),3))
    dwt.RMSE <- c(dwt.RMSE, round(sqrt(mean((m2-x)^2)),3))
  
    sd.val <- cbind(sd.val, as.vector(c(sd(x),sd(m1),sd(m2))))
    cor.val <- cbind(cor.val, cor(cbind(x,m1,m2))[,1])
  
    df1 <- data.frame(Group=1, s=s[i], No=1:sample.val,Pred=m1, Obs=x)
    df2 <- data.frame(Group=2, s=s[i], No=1:sample.val,Pred=m2, Obs=x)
  
    df.val <- rbind(df.val, rbind(df1,df2))
  
  }
  
  #summary(df.val)
  #print(rbind(data.RMSE,dwt.RMSE))
  
  t2 <- rbind(data.RMSE,dwt.RMSE)
  sd.val;cor.val
  
  ###standard method----------------------------------------------------------
  # form new response and predictors dataset - calibration
  data.list <- list()
  for(i in 1:length(ts.list)){
    #i <- 1
    x <- ts.list[[i]]$x[1:sample.cal]
    y <- ts.list[[i]]$y[1:sample.cal]
    z <- ts.list[[i]]$z[1:sample.cal]
    
    xx <- padding(x, pad); yy <- padding(y, pad)
    n <- length(x)
    
    J <- floor(log10(n)) # (Nourani et al., 2008)
    print(paste0("Direct wavelet approach: Decomposition Levels J= ",J))

    if(mode=="MRA"){
    mra.x <- matrix(unlist(lapply(mra(xx,wf,J,method,boundary), function(z) z[1:n])), ncol=J+1, byrow=FALSE)
    mra.y <- matrix(unlist(lapply(mra(yy,wf,J,method,boundary), function(z) z[1:n])), ncol=J+1, byrow=FALSE)
    } else if(mode=="MODWT"){
    mra.x <- matrix(unlist(lapply(modwt(x,wf,J,boundary), function(z) z)), ncol=J+1, byrow=FALSE)
    mra.y <- matrix(unlist(lapply(modwt(y,wf,J,boundary), function(z) z)), ncol=J+1, byrow=FALSE)      
    } else {
    mra.x <- matrix(unlist(at.wd(x,wf,J,boundary)), ncol=J+1)
    mra.y <- matrix(unlist(at.wd(y,wf,J,boundary)), ncol=J+1)
    }
    
    data.list[[i]] <- list(x=as.numeric(z), dp=cbind(mra.x, mra.y))
  }
  
  #----------------------------------------------------
  #calibration
  df <- NULL;data.RMSE<-NULL
  for(i in 1:length(data.list)){
    dwt <- data.list[[i]]
    x <- dwt$x; dp <- dwt$dp
    
    m <- FNN::knn.reg(train=dp, y=x, k=k)$pred
    data.RMSE <-c(data.RMSE, round(sqrt(mean((m-x)^2)),3))
    df <- data.frame(Group=1, s=s[i], No=1:sample.cal,Pred=m, Obs=x)
  }
  
  #----------------------------------------------------
  # form new response and predictors dataset -  validation
  data.list.val <- list()
  for(i in 1:length(ts.list)){
    #i <- 1
    x <- ts.list[[i]]$x[(sample.cal+1):sample]
    y <- ts.list[[i]]$y[(sample.cal+1):sample]
    z <- ts.list[[i]]$z[(sample.cal+1):sample]
    
    xx <- padding(x, pad); yy <- padding(y, pad)
    n <- length(x)
    
    J <- floor(log10(n)) # (Nourani et al., 2008)
    
    if(mode=="MRA"){
    mra.x <- matrix(unlist(lapply(mra(xx,wf,J,method,boundary), function(z) z[1:n])), ncol=J+1, byrow=FALSE)
    mra.y <- matrix(unlist(lapply(mra(yy,wf,J,method,boundary), function(z) z[1:n])), ncol=J+1, byrow=FALSE)
    } else if(mode=="MODWT"){
    mra.x <- matrix(unlist(lapply(modwt(x,wf,J,boundary), function(z) z)), ncol=J+1, byrow=FALSE)
    mra.y <- matrix(unlist(lapply(modwt(y,wf,J,boundary), function(z) z)), ncol=J+1, byrow=FALSE)      
    } else {
    mra.x <- matrix(unlist(at.wd(x,wf,J,boundary)), ncol=J+1)
    mra.y <- matrix(unlist(at.wd(y,wf,J,boundary)), ncol=J+1)
    }

    data.list.val[[i]] <- list(x=as.numeric(z), dp=cbind(mra.x, mra.y))
  }
  
  #----------------------------------------------------
  #validation
  sample.val <- sample-sample.cal
  df.val <- NULL;dwt.RMSE<-NULL
  for(i in 1:length(data.list.val)){
    dwt <- data.list[[i]]
    x.train <- dwt$x; dp <- dwt$dp
    
    dwt <- data.list.val[[i]]
    x <- dwt$x; dp.v <- dwt$dp
    
    m <- FNN::knn.reg(train=dp, test=dp.v, y=x.train, k=k)$pred
    dwt.RMSE <-c(dwt.RMSE, round(sqrt(mean((m-x)^2)),3))
    df.val <- data.frame(Group=1, s=s[i], No=1:sample.val,Pred=m, Obs=x)
  }
  
  t3 <- rbind(data.RMSE,dwt.RMSE)
  
  #----------------------------------------------------
  #comparison
  df.RMSE <- rbind(rbind(t1,t2),t3)
  rownames(df.RMSE) <- NULL
  df.RMSE.n <- data.frame(Method=mode,
    Group=c("Calibration", "Calibration", "Validation", "Validation", 
            "Calibration", "Validation"),
    Model = c("Original", "VT", "Original", "VT", "Wavelet-decomposed components", 
              "Wavelet-decomposed components"),df.RMSE)%>%
    tidyr::gather(S,Value,4:(3+length(s)))%>% tidyr::spread(Group, Value)
  
  tab3 <- rbind(tab3,df.RMSE.n[order(df.RMSE.n$S),])

}

#----------------------------------------------------
kable(tab3[,-3], caption= "Comparison of three methods using original predictor, 
      wavelet-decomposed components, and variance-transformed predictor", booktabs = T)%>%
kable_styling("striped", position = "center", full_width = FALSE) %>% 
collapse_rows(columns = 1, valign = "middle")
```
