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http://dx.doi.org/10.3741/JKWRA.2005.38.8.617

Improved Trend Estimation of Non-monotonic Time Series Through Increased Homogeneity in Direction of Time-variation  

Oh, Kyoung-Doo (Dept. of Civil Engineering, Korea Military Academy)
Park, Soo-Yun (Hanjin Information Systems & Telecommunication Co., Ltd.)
Lee, Soon-Cheol (Dept. of Civil Engineering, Suwon University)
Jun, Byong-Ho (Dept. of Civil Engineering, Korea Military Academy)
Ahn, Won-Sik (Dept. of Civil Engineering, Suwon University)
Publication Information
Journal of Korea Water Resources Association / v.38, no.8, 2005 , pp. 617-629 More about this Journal
Abstract
In this paper, a hypothesis is tested that division of non-monotonic time series into monotonic parts will improve the estimation of trends through increased homogeneity in direction of time-variation using LOWESS smoothing and seasonal Kendall test. From the trend analysis of generated time series and water temperature, discharge, air temperature and solar radiation of Lake Daechung, it is shown that the hypothesis is supported by improved estimation of trends and slopes. Also, characteristics in homogeneity variation of seasonal changes seems to be more clearly manifested as homogeneity in direction of time-variation is increased. And this will help understand the effects of human intervention on natural processes and seems to warrant more in-depth study on this subject. The proposed method can be used for trend analysis to detect monotonic trends and it is expected to improve understanding of long-term changes in natural environment.
Keywords
Locally weighted regression smoothing; Trend analysis; Non-parametric statistics; Seasonal Kendall test; Time series;
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