Titlebook: Geodetic Time Series Analysis in Earth Sciences; Jean-Philippe Montillet,Machiel S. Bos Book 2020 Springer Nature Switzerland AG 2020 GNSS

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https://doi.org/10.1007/978-3-642-49179-5t these time series also exhibit colored noise. In this chapter we present noise models for these geodetic time series such as the commonly used first order Auto Regressive (AR), the General Gauss Markov (GGM) and the ARFIMA model. The theory is applied to GNSS and tide gauge data from the Pacific Northwest coast.

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The Art and Science of Trajectory Modelling,lso illuminate the diversity of ways in which the Earth moves and deforms. We distinguish between the deterministic approach to trajectory modelling, which emphasizes the physical meaning of the various components of the trajectory, and a more automatic, autonomous and heuristic approach to finding and fitting a trajectory model.

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Die Luftvorwärmung im Dampfkesselbetriebncluding ARMA models and standard multiple linear regression models. The models can be seen as general regression models where the coefficients can vary in time. In addition, they allow for a state space representation and a formulation as hierarchical statistical models, which in turn is the key fo

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,Schwierigkeiten des Heißluftbetriebes,orrelations. One such assessment is based on the characteristics of the time series residuals averaged over different durations and with the statistical characteristics extrapolated with a first-order Gauss–Markov process to infinite averaging time. This approach circumvents a limitation of spectral

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