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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Abstract

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
URI:	http://35.240.28.64/id/eprint/1417
Author Links:	[error in script] No links available.

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