Time Series Data Cleaning: A Survey

Abstract

Errors are prevalent in time series data, which is particularly common in the industrial field. Data with errors could not be stored in the database, which results in the loss of data assets. At present, to deal with these time series containing errors, besides keeping original erroneous data, discarding erroneous data and manually checking erroneous data, we can also use the cleaning algorithm widely used in the database to automatically clean the time series data. This survey provides a classification of time series data cleaning techniques and comprehensively reviews the state-of-the-art methods of each type. Besides we summarize data cleaning tools, systems and evaluation criteria from research and industry. Finally, we highlight possible directions time series data cleaning.