Cyclic clustering approach to impute missing values for cyclostationary hydrological time series

Mohammad Reza Mahmoudi ()
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Mohammad Reza Mahmoudi: Fasa University

Quality & Quantity: International Journal of Methodology, 2023, vol. 57, issue 3, No 31, 2619-2639

Abstract: Abstract In different scientific fields, some parts of the collected dataset may include incomplete data with at least one missing value. To tackle this issue, statisticians and machine learning experts have introduced different missing value imputation (MVI) approaches. Hydrological data are mostly time series with stationary (or cyclic) trends that its observations have high autocorrelation (or cyclic auto-correlation). In addition, these time series frequently exhibit random and non-constant oscillations from their trends. Therefore, the selected MVI approach for hydrological time series should consider these serious aspects. One aspect of hydrological time series data that has not been closely examined in the MVI studies up to now is the existence of cyclic trend, named cyclostationarity. In this paper, a novel MVI approach for cyclostationary hydrological time series, called cyclic clustering, will be discussed. The performance of the proposed method is studied by employing numerous simulated datasets. The results indicate that the cyclic clustering approach is robust to impute missing values for both stationary and cyclostationary hydrological time series. The applicability of the introduced approach is also investigated in a real-world problem.

Keywords: Missing data; Missing value; Imputation; Hydrological time series; Cyclostationary time series; Clustering (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s11135-022-01484-9

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