  Data cleaning is a crucial part of every data analysis exercise. Yet, the
currently available R packages do not provide fast and robust methods for
cleaning and preparation of time series data. The open source package tsrobprep
introduces efficient methods for handling missing values and outliers using
model based approaches. For data imputation a probabilistic replacement model
is proposed, which may consist of autoregressive components and external
inputs. For outlier detection a clustering algorithm based on finite mixture
modelling is introduced, which considers typical time series related properties
as features. By assigning to each observation a probability of being an
outlying data point, the degree of outlyingness can be determined. The methods
work robust and are fully tunable. Moreover, by providing the
auto_data_cleaning function the data preprocessing can be carried out in one
cast, without manual tuning and providing suitable results. The primary
motivation of the package is the preprocessing of energy system data, however,
the package is also suited for other moderate and large sized time series data
set. We present application for electricity load, wind and solar power data.
