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

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 tak...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2021-10
Main Authors: Mayur Kishor Shende, Feijoo-Lorenzo, Andres E, Bokde, Neeraj Dhanraj
Format: Article
Language:English
Subjects:
Automation
Cleaning
Data analysis
Decision analysis
Microbalances
Time series
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2331-8422
DOI:10.48550/arxiv.2110.11815