延展
发表于 2025-3-21 19:23:45
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Vasoconstrictor
发表于 2025-3-21 21:11:36
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Androgen
发表于 2025-3-22 03:53:18
A Nearest Neighbor Approach to Forecast Nonlinear Time Series,idorowich , to improve the short term prediction of nonlinear chaotic processes. The idea underlying their forecasting algorithm is as follows: For a nonlinear low-dimensional process, a state space reconstruction of the observed time series exhibits “spatial” correlation, which can be exploit
maverick
发表于 2025-3-22 04:36:06
Conclusions and Outlook,s are a priori linear in nature — provides a real enrichment of what simple dynamical systems in economics can explain. A model which exhibits simple regular behavior in its linear version may perform completely different if it is reformulated to include nonlinearities.
orthodox
发表于 2025-3-22 12:06:41
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Spongy-Bone
发表于 2025-3-22 13:36:04
Book 1995 One of the main reasons why nonlinear dynamic models are so interesting to economists is that they are able to produce a great variety of possible dynamic outcomes - from regular predictable behavior to the most complex irregular behavior - rich enough to meet the economists‘ objectives of modeling
轻率看法
发表于 2025-3-22 17:54:07
0075-8442 ossible dynamic outcomes - from regular predictable behavior to the most complex irregular behavior - rich enough to meet the economists‘ objectives of modeling978-3-540-59374-4978-3-642-46821-6Series ISSN 0075-8442 Series E-ISSN 2196-9957
SPURN
发表于 2025-3-23 00:51:58
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bifurcate
发表于 2025-3-23 04:59:51
A Nearest Neighbor Approach to Forecast Nonlinear Time Series, test to accompany the algorithm is suggested here. To demonstrate its practical use, the methodology is applied to the observed agricultural price series. It is found that the short term predictability of the best fitting linear model can be improved upon significantly by this method.
彩色的蜡笔
发表于 2025-3-23 06:46:33
Book 1995mmetric and asymmetric. There exist two different perspectives to explain this kind of behavior within the framework of a dynamical model. The traditional belief is that the time evolution of the series can be explained by a linear dynamic model that is exogenously disturbed by a stochastic process.