Adding imputation methods

Author: JBris

r/Dockerfile

@@ -5,4 +5,5 @@ FROM rocker/tidyverse:$TIDYVERSE_TAG
 RUN install2.r --error \
     --deps TRUE \
     anomalize \
-    coindeskr
+    coindeskr \
+    imputeTS \

r/user/extras/ts_imputation.r

@@ -0,0 +1,80 @@
+# Source: https://www.kaggle.com/juejuewang/handle-missing-values-in-time-series-for-beginners
+# Author: Nishant Singh - https://www.datacamp.com/profile/NishantKumarSingh
+
+library(imputeTS)
+
+View(tsAirgap)
+
+plot(tsAirgap, main="AirPassenger data with missing values")
+
+statsNA(tsAirgap)
+
+#########################################################################################
+
+# General imputation methods
+
+par(mfrow=c(2,2))
+# Mean Imputation
+plot(na_mean(tsAirgap, option = "mean") - AirPassengers, ylim = c(-mean(AirPassengers), mean(AirPassengers)), ylab = "Difference", main = "Mean")
+mean((na_mean(tsAirgap, option = "mean") - AirPassengers)^2)
+
+# Median Imputation
+plot(na_mean(tsAirgap, option = "median") - AirPassengers, ylim = c(-mean(AirPassengers), mean(AirPassengers)), ylab = "Difference", main = "Median")
+mean((na_mean(tsAirgap, option = "median") - AirPassengers)^2)
+
+# Mode Imputation
+plot(na_mean(tsAirgap, option = "mode") - AirPassengers, ylim = c(-mean(AirPassengers), mean(AirPassengers)), ylab = "Difference", main = "Mode")
+mean((na_mean(tsAirgap, option = "mode") - AirPassengers)^2)    
+
+# Random Imputation
+plot(na_random(tsAirgap) - AirPassengers, ylim = c(-mean(AirPassengers), mean(AirPassengers)), ylab = "Difference", main = "Random")
+mean((na_random(tsAirgap) - AirPassengers)^2)
+
+#########################################################################################
+
+#TS specific imputation methods
+
+par(mfrow=c(2,2))
+# Last Observartion Carried Forward
+plot(na_locf(tsAirgap, option = "locf") - AirPassengers, ylim = c(-mean(AirPassengers), mean(AirPassengers)), ylab = "Difference", main = "LOCF")
+m1 <- mean((na_locf(tsAirgap, option = "locf") - AirPassengers)^2)
+
+# Next Observartion Carried Backward
+plot(na_locf(tsAirgap, option = "nocb") - AirPassengers, ylim = c(-mean(AirPassengers), mean(AirPassengers)), ylab = "Difference", main = "NOCB")
+m2 <- mean((na_locf(tsAirgap, option = "nocb") - AirPassengers)^2)
+
+# Linear Interpolation
+plot(na_interpolation(tsAirgap, option = "linear") - AirPassengers, ylim = c(-mean(AirPassengers), mean(AirPassengers)), ylab = "Difference", main = "Linear")
+m3 <- mean((na_interpolation(tsAirgap, option = "linear") - AirPassengers)^2)
+
+# Spline Interpolation
+plot(na_interpolation(tsAirgap, option = "spline") - AirPassengers, ylim = c(-mean(AirPassengers), mean(AirPassengers)), ylab = "Difference", main = "Spline")
+
+m4 <- mean((na_interpolation(tsAirgap, option = "spline") - AirPassengers)^2)
+
+data.frame(methods=c('LOCF', 'NACB', 'Linear', 'Spline'), MSE=c(m1, m2, m3, m4))
+
+#########################################################################################
+
+#Combined imputation approach
+
+par(mfrow=c(2,2))
+# Seasonal Adjustment then Random
+plot(na_seadec(tsAirgap, algorithm = "random") - AirPassengers, ylim = c(-mean(AirPassengers), mean(AirPassengers)), ylab = "Difference", main = "Seas-Adj -> Random")
+ma1 <- mean((na_seadec(tsAirgap, algorithm = "random") - AirPassengers)^2)
+
+# Seasonal Adjustment then Mean
+plot(na_seadec(tsAirgap, algorithm = "mean") - AirPassengers, ylim = c(-mean(AirPassengers), mean(AirPassengers)), ylab = "Difference", main = "Seas-Adj -> Mean")
+ma2 <- mean((na_seadec(tsAirgap, algorithm = "mean") - AirPassengers)^2)
+
+# Seasonal Adjustment then LOCF
+plot(na_seadec(tsAirgap, algorithm = "locf") - AirPassengers, ylim = c(-mean(AirPassengers), mean(AirPassengers)), ylab = "Difference", main = "Seas-Adj -> LOCF")
+ma3 <- mean((na_seadec(tsAirgap, algorithm = "locf") - AirPassengers)^2)
+
+# Seasonal Adjustment then Linear Interpolation
+plot(na_seadec(tsAirgap, algorithm = "interpolation") - AirPassengers, ylim = c(-mean(AirPassengers), mean(AirPassengers)), ylab = "Difference", main = "Seas-Adj -> Linear")
+
+ma4 <- mean((na_seadec(tsAirgap, algorithm = "interpolation") - AirPassengers)^2)
+
+data.frame(methods=c("Seas-Adj+Random", "Seas-Adj+Mean", "Seas-Adj+LOCF","Seas-Adj+Linear"),
+           MSE=c(ma1, ma2, ma3, ma4))