# R-miss-tastic

A resource website on missing values - Methods and references for managing missing data

imputeTS

##### Category:

Time-Series Imputation, Visualisations for Missing Data

##### Use-Cases:

Imputation for univariate time series, Imputation of Sensor data, Visualization of time series missing data

##### Description:

Imputation (replacement) of missing values in univariate time series. Offers several imputation functions and missing data plots. Available imputation algorithms include: ‘Mean’, ‘LOCF’, ‘Interpolation’, ‘Moving Average’, ‘Seasonal Decomposition’, ‘Kalman Smoothing on Structural Time Series models’, ‘Kalman Smoothing on ARIMA models’.

#### Last update:

##### Algorithms:
• Mean imputation (mean, mode, median)
• Last observation carried forward (locf)
• Next observation carried backward (nocb)
• Linear interpolation
• Spline interpolation
• Stineman interpolation
• Simple Moving Average
• Linear Weighted Moving Average
• Exponentially Weighted Moving Average
• Seasonal Decomposition based imputation
• Seasonal Split based imputation
• Kalman Smoothing on Structural Time Series models
• Kalman Smoothing on ARIMA models’
##### Datasets:
• tsAirgap (airpass dataset - Monthly totals of international airline passengers, 1949 to 1960)
• tsNH4 (Time series of NH4 concentration in a wastewater system)
• tsHeating (Time series of a heating systems supply temperature)
##### Input:

vector, ts, data.frame, zoo, xts

##### Example:
library(imputeTS)

print("print time-series with NAs")
print(tsAirgap)

#perform the missing value replacement
imp <- na.mean(tsAirgap)

print("print the series with the imputations")
print(imp)

#Visualize the imputations
plotNA.imputations(imp, x.withNA = tsAirgap)gap)


Here you can have a interactive look at the example:

https://rdrr.io/snippets/embedding/