Representing financial time series based on important extrema points
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
A review on time series data mining
Engineering Applications of Artificial Intelligence
Brief paper: Frequency domain approach for designing sampling rates for system identification
Automatica (Journal of IFAC)
Paper: Structural parameter estimation in power systems
Automatica (Journal of IFAC)
Special section system identification tutorial: Maximum likelihood and prediction error methods
Automatica (Journal of IFAC)
Survey of applications of identification in chemical and physical processes
Automatica (Journal of IFAC)
A problem in hydrological model calibration in the case of averaged flux input and flux output
Environmental Modelling & Software
An approach to dimensionality reduction in time series
Information Sciences: an International Journal
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Aliasing gives a lower bound for the sampling rate in ordinary spectral analysis of a time series. In parametric it appears at first sight that no such limitations are present. In this note we will obtain insight into this paradox by analyzing a simple Gauss-Markov process. We assume that a time series analysis is performed based on N samples of the series at equal spacing h. The result shows that there is an optimal choice of h and that the variance increases rapidly when h increases from the optimal value. The analysis of a time series of fixed length T with different number of samplings is also discussed.