Results 281 to 290 of about 10,612,437 (315)

Detecting dynamical nonstationarity in time series data

Chaos: An Interdisciplinary Journal of Nonlinear Science, 1999
Nonlinear time series analysis is becoming an ever more powerful tool to explore complex phenomena and uncover underlying patterns from irregular data recorded from experiments. However, the existence of dynamical nonstationarity in time series data causes many results of such analysis to be questionable and inconclusive.
Yu, Dejin, Lu, Weiping, Harrison, Robert G.   +2 more
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Diagnosing time series dynamical structures

Chaos, Solitons & Fractals, 1996
Abstract A sequence of two tests is proposed to diagnose time series dynamical structures. The first is known as the BDS test. It determines whether a series that is free from linear structure is independently and identically distributed noise (iid). If the series is not iid, a β test (proposed in this paper) associates the structure with the series'
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Dynamic charge equalisation for series-connected batteries

IEE Proceedings - Electric Power Applications, 2003
A charge equalisation circuit is proposed for the series-connected battery bank in a serial charging scheme. The charge equalisation is based on buck–boost conversion, and hence is essentially nondissipative. The subcircuit associated with each battery acts as a current diverter to transfer the excess charging current from the fully charged batteries ...
Y.C. Hsieh, C.S. Moo, I.S. Tsai, J.C. Cheng   +3 more
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Time Series and Dynamic Models

1996
In this book Christian Gourieroux and Alain Monfort provide an up-to-date and comprehensive analysis of modern time series econometrics. They have succeeded in synthesising in an organised and integrated way a broad and diverse literature. While the book does not assume a deep knowledge of economics, one of its most attractive features is the close ...
Christian Gourieroux, Alain Monfort
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Dynamic graph structure learning for multivariate time series forecasting

Pattern Recognition, 2023
Zhuoling Li, Gaowei Zhang, Jie Yu, Lingyu Xu   +3 more
semanticscholar   +1 more source

Extraction of stochastic dynamics from time series

Physical Review E, 2012
We present a method for the reconstruction of the dynamics of processes with discrete time. The time series from such a system is described by a stochastic recurrence equation, the continuous form of which is known as the Langevin equation. The deterministic f and stochastic g components of the stochastic equation are directly extracted from the ...
M, Petelczyc, J J, Żebrowski, J M, Gac
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Bayesian optimization based dynamic ensemble for time series forecasting

Information Sciences, 2022
Liang Du, Ruobin Gao, P. Suganthan, David Z. W. Wang   +3 more
semanticscholar   +1 more source

Series of Dynamic Targeted Recommendations

2002
Merchants often use marketing elements such as advertisements, coupons and product recommendations, to attract customers and to convert visitors to buyers. We present a model for making a series of recommendations during a customer session. The model comprises of the customer's probability of accepting a marketing element from a marketing spot and a ...
Natwar Modani, Parul A. Mittal, Amit A. Nanavati, Biplav Srivastava   +3 more
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NONLINEAR DYNAMICS ANALYSIS OF TRAFFIC TIME SERIES

Modern Physics Letters B, 2004
Traffic flow shows various complex behaviors. Numerous empirical data and numerical simulation results demonstrate the existence of distinct dynamics states. In order to obtain insight into the nature of the dynamics, we apply the nonlinear time series analysis approach to study the characteristic behavior of traffic flow at low and intermediate ...
Li, Keping, Gao, Ziyou
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Decomposition of Time Series Dynamic Linear Models

Journal of Time Series Analysis, 2003
This paper derives the admissible decompositions for a time series dynamic linear model, assuming only that the model is observable. The decompositions depend on factorizations of the characteristic polynomial of the state evolution matrix G into relatively prime factors.
Godolphin, E. J., Jonson, S. E.
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