Two preprocessing algorithms for climate time series

Schlüter, Stephan and Kresoja, Milena (2019) Two preprocessing algorithms for climate time series. Journal of Applied Statistics. ISSN 0266-4763

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We propose two preprocessing algorithms suitable for climate time series. The first algorithm detects outliers based on an autoregressive cost update mechanism. The second one is based on the wavelet transform, a methodfrom pattern recognition. In order to benchmark the algorithms’ performance we compare them to existing methods based on a synthetic data set. Eventually, for exemplary purposes, the proposed methods are applied to a data set of high-frequent temperature measurements from Novi Sad, Serbia. The results show that both methods together form a powerful tool for signal preprocessing: In case of solitary outliers the autoregressive cost update mechanism prevails, whereas the wavelet-based mechanism is the method of choice in the presence of multiple consecutive outliers.

Item Type: Article
Additional Information: COBISS.ID=512596066
Uncontrolled Keywords: data preprocessing, outliers, temperature, wavelets
Research Department: Sustainable Development
Depositing User: Jelena Banovic
Date Deposited: 03 Feb 2020 10:09
Last Modified: 31 Mar 2021 08:20

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