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Books > Reference & Interdisciplinary > Communication studies > Information theory > Cybernetics & systems theory
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Nonstationarities in Hydrologic and Environmental Time Series (Paperback, Softcover reprint of the original 1st ed. 2003)
Loot Price: R4,377
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Nonstationarities in Hydrologic and Environmental Time Series (Paperback, Softcover reprint of the original 1st ed. 2003)
Series: Water Science and Technology Library, 45
Expected to ship within 10 - 15 working days
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Most of the time series analysis methods applied today rely heavily
on the key assumptions of linearity, Gaussianity and stationarity.
Natural time series, including hydrologic, climatic and
environmental time series, which satisfy these assumptions seem to
be the exception rather than the rule. Nevertheless, most time
series analysis is performed using standard methods after relaxing
the required conditions one way or another, in the hope that the
departure from these assumptions is not large enough to affect the
result of the analysis.
A large amount of data is available today after almost a century of
intensive data collection of various natural time series. In
addition to a few older data series such as sunspot numbers, sea
surface temperatures, etc., data obtained through dating techniques
(tree-ring data, ice core data, geological and marine deposits,
etc.), are available. With the advent of powerful computers, the
use of simplified methods can no longer be justified, especially
with the success of these methods in explaining the inherent
variability in natural time series.
This book presents a number of new techniques that have been
discussed in the literature during the last two decades concerning
the investigation of stationarity, linearity and Gaussianity of
hydrologic and environmental times series. These techniques cover
different approaches for assessing nonstationarity, ranging from
time domain analysis, to frequency domain analysis, to the combined
time-frequency and time-scale analyses, to segmentation analysis,
in addition to formal statistical tests of linearity and
Gaussianity. It is hoped that this endeavor would facilitate
further research into this important area.
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