Publication: Continuous Imputation of Missing Values in Streams of Pattern-Determining Time Series
Continuous Imputation of Missing Values in Streams of Pattern-Determining Time Series
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Wellenzohn, K., Böhlen, M. H., Dignös, A., Gamper, J., & Mitterer, H. (2017). Continuous Imputation of Missing Values in Streams of Pattern-Determining Time Series. 330–341. https://doi.org/10.5441/002/edbt.2017.30
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Time series data is ubiquitous but often incomplete, e.g., due to sensor failures and transmission errors. Since many applications require complete data, missing values must be imputed before further data processing is possible. We propose Top-k Case Matching (TKCM) to impute missing values in streams of time series data. TKCM defines for each time series a set of reference time series and exploits similar historical situations in the reference time series for the imputation. A situation is characterized by the anchor point of a patte
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- Wellenzohn, Kevin
- Böhlen, Michael H
- Dignös, Anton
- Gamper, Johann
- Mitterer, Hannes
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Wellenzohn, K., Böhlen, M. H., Dignös, A., Gamper, J., & Mitterer, H. (2017). Continuous Imputation of Missing Values in Streams of Pattern-Determining Time Series. 330–341. https://doi.org/10.5441/002/edbt.2017.30
