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Short-term Variation Analysis on the Time-series Dissolved Oxygen Data Using the Variance Measures of the Time-difference Variables

시간차분 변수의 분산측도를 이용한 시계열 용존산소 자료의 단기 변동 분석

TaeKyun Kim12
,
HongYeon Cho12
,
GiSeop Lee2*
김 태균12
,
조 홍연12
,
이 기섭2*
1Major of Ocean Convergence Engineering, University of Science and Technology, Daejeon 34113, Korea
2Marine Bigdata･A.I. Center, Korea Institute of Ocean Science & Technology, Busan 49111, Korea
1과학기술연합대학원대학교 해양융합공학 전공 (34113) 대전광역시 유성구 가정로 217
2한국해양과학기술원 해양빅데이터･A.I.센터 (49111) 부산광역시 영도구 해양로 385
*Corresponding Author
18 April 2025. pp. 1-13
PDF XML Citation
Abstract

This study presents a method for analyzing short-term fluctuations in time-series (TS) data, which is essential for ocean monitoring. The proposed approach utilizes statistical measures from time-differenced variables defined across various time lags, linking the standard deviation (SD) with the autocorrelation function (ACF). This connection facilitates the exploration of variability and stationarity. Focusing on variations over a 7-day period, this method is applied to dissolved oxygen concentration data collected from the marine water quality monitoring network at the Nakdong Myeongji and Eulsuk stations, provided by the Korea Marine Environment Management Corporation (KOEM). The findings suggest that the standard deviation of differenced variables exhibits fluctuation patterns consistent with those predicted by the autocorrelation theory. Rapid increases are observed within a time frame of less than 6 hours, followed by gradual changes over longer time scales. Additionally, a periodic decline is noted after approximately 12 hours on a daily scale, indicating cyclic fluctuations within the daily cycle. In conjunction with the standard deviation, robust dispersion measures are introduced to highlight the differences, with a specific emphasis on the median absolute deviation to evaluate the normality of ocean observation data. Consequently, the proposed technique serves as a valuable quantitative analytical tool for assessing diurnal variability in dissolved oxygen concentrations and for testing the assumptions of stationarity and normality in time series data.

Keywords
time-series monitoring data
time-difference variables
spreading parameter variation
stationarity
normality
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Information
  • Publisher :Korea Institute of Ocean Science and Technology
  • Publisher(Ko) :한국해양과학기술원
  • Journal Title :Ocean and Polar Research
  • Journal Title(Ko) :Ocean and Polar Research
  • Volume : 47
  • Pages :1-13
  • Received Date : 2024-12-10
  • Revised Date : 2025-03-11
  • Accepted Date : 2025-04-02
  • Published Date : 2025-04-18
  • DOI :https://doi.org/10.4217/OPR.2025004
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