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Data cleaning is a crucial part of every data analysis exercise. Yet, thecurrently available R packages do not provide fast and robust methods forcleaning and preparation of time series data. The open source package tsrobprepintroduces efficient methods for handling missing values and outliers usingmodel based approaches. For data imputation a probabilistic replacement modelis proposed, which may consist of autoregressive components and externalinputs. For outlier detection a clustering algorithm based on finite mixturemodelling is introduced, which considers time series properties in terms of thegradient and the underlying seasonality as features. The procedure allows toreturn a probability for each observation being outlying data as well as aspecific cause for an outlier assignment in terms of the provided featurespace. The methods work robust and are fully tunable. Moreover, by providingthe auto_data_cleaning function the data preprocessing can be carried out inone cast, without comprehensive tuning and providing suitable results. Theprimary motivation of the package is the preprocessing of energy system data.We present application for electricity load, wind and solar power data.

tsrobprep - an R package for robust preprocessing of time series data