  Data cleaning is one of the most important tasks in data analysis processes.
One of the perennial challenges in data analytics is the detection and handling
of non-valid data. Failing to do so can result in inaccurate analytics and
unreliable decisions. The process of properly cleaning such data takes much
time. Errors are prevalent in time series data. It is usually found that real
world data is unclean and requires some pre-processing. The analysis of large
amounts of data is difficult. This paper is intended to provide an easy to use
and reliable system which automates the cleaning process of univariate time
series data. Automating the process greatly reduces the time required.
Visualizing a large amount of data at once is not very effective. To tackle
this issue, an R package cleanTS is proposed. The proposed system provides a
way to analyze data on different scales and resolutions. Also, it provides
users with tools and a benchmark system for comparing various techniques used
in data cleaning.
