  Errors are prevalent in time series data, especially in the industrial field.
Data with errors could not be stored in the database, which results in the loss
of data assets. Handling the dirty data in time series is non-trivial, when
given irregular time intervals. 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. In particular, we have a special focus on the irregular time intervals.
Besides we summarize data cleaning tools, systems and evaluation criteria from
research and industry. Finally, we highlight possible directions time series
data cleaning.
