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

Abstract

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.

The cleanTS package is available in CRAN (https://cran.r-project.org/package=cleanTS)

References in corpus (5)

• Short-term CO2 emissions forecasting based on decomposition approaches and its impact on electricity market scheduling
• ForecastTB An R Package as a Test-Bench for Time Series Forecasting Application of Wind Speed and Solar Radiation Modeling
• HoloClean: Holistic Data Repairs with Probabilistic Inference
• A graphical approach to carbon-efficient spot market scheduling for Power-to-X applications
• An empirical estimation for time and memory algorithm complexities: newly developed R package

Cited by in corpus (2)

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