gistfile1.txt

plot(ts(mymts[,1]))
# DWT with level of 2^3
data_dwt <- repr_dwt(mymts[,1], level = 6); length(data_dwt)
# first 84 DFT coefficients are extracted and then inverted
data_dft <- repr_dft(mymts[,1], coef = 63); length(data_dft)
# first 84 DCT coefficients are extracted and then inverted
data_dct <- repr_dct(mymts[,1], coef = 63); length(data_dct)
# Classical PAA
data_paa <- repr_paa(mymts[,1], q = 65, func = mean); length(data_paa)

data_plot <- data.frame(Value = c(data_dwt, data_dft, data_dct, data_paa),
                        Time = rep(1:length(data_dwt), 4),
                        Method = factor(rep(c("DWT", "DFT", "DCT", "PAA"),
                                            each = length(data_dwt))))
ggplot(data_plot, aes(Time, Value, color = Method)) +
  geom_line(alpha = 0.80, size = 0.8) + theme_bw()


data_lm <- repr_lm(mymts[,1], freq = 63, method = "lm"); length(data_lm)
# robust linear model and l1 regression are also implemented
data_l1 <- repr_lm(mymts[,1], freq = 63, method = "l1"); length(data_l1)
# GAM
data_gam <- repr_gam(mymts[,1], freq = 63); length(data_gam)
# median seasonal profile
data_seas_prof <- repr_seas_profile(mymts[,1], freq = 63, func = median); length(data_seas_prof )
# exponential smoothing
data_exp <- repr_exp(mymts[,1], freq = 63); length(data_exp)

data_plot <- data.frame(Value = c(data_lm, data_l1, data_seas_prof, data_exp, data_gam),
                        Time = c(rep(1:length(data_lm), 4), 1:length(data_gam)),
                        Method = c(rep(c("LM", "L1", "Median seas. prof.", "Exp. smooth."),
                                       each = 63), rep("GAM", 62)))

ggplot(data_plot, aes(Time, Value, color = Method)) +
  geom_line(alpha = 0.80, size = 0.8) +
  theme_bw()

data_pip <- repr_pip(mymts[,1], times = 60, return = "both")
data_pla <- repr_pla(mymts[,1], times = 60, return = "both")
data_plot <- data.frame(Value = c(mymts[,1], data_pip$points, data_pla$points),
                        Time = c(1:length(mymts[,1]), data_pip$places, data_pla$places),
                        Method = c(rep("Original", length(mymts[,1])),
                                   rep(c("PIP", "PLA"), each = length(data_pla$places))))
ggplot(data_plot, aes(Time, Value, color = Method)) +
  geom_line(alpha = 0.65, size = 0.8) +
  theme_bw()