Modeling and Predicting Non-Stationary Time Series
1997 ◽
Vol 07
(08)
◽
pp. 1823-1831
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Keyword(s):
Many experimental time series are non-stationary. Modeling and predicting them is generally considered to be difficult. In this paper we introduce time-dependent regressive (TDR) models, which depend not only on system states but also on time. We test artificial time series which come from parameter-changing systems and are therefore non-stationary, and a simulated experimental time series from a model of a non-stationary industrial system. The TDR models work well on those time series, not only in prediction but also in extraction of the underlying bifurcations.
1970 ◽
Vol 32
(2)
◽
pp. 312-322
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Keyword(s):
1976 ◽
Vol 23
(5)
◽
pp. 647-656
◽
Keyword(s):
Keyword(s):
1975 ◽
Vol 4
(1)
◽
pp. 19-32
Keyword(s):
2021 ◽
pp. 125920
Keyword(s):