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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 handlingof non-valid data. Failing to do so can result in inaccurate analytics andunreliable decisions. The process of properly cleaning such data takes muchtime. Errors are prevalent in time series data. It is usually found that realworld data is unclean and requires some pre-processing. The analysis of largeamounts of data is difficult. This paper is intended to provide an easy to useand reliable system which automates the cleaning process of univariate timeseries data. Automating the process greatly reduces the time required.Visualizing a large amount of data at once is not very effective. To tacklethis issue, an R package cleanTS is proposed. The proposed system provides away to analyze data on different scales and resolutions. Also, it providesusers with tools and a benchmark system for comparing various techniques usedin data cleaning.

cleanTS: Automated (AutoML) Tool to Clean Univariate Time Series at Microscales