Sentence examples for modelling time series from inspiring English sources

These feature vectors are obtained by modelling time series data by Gaussian distributions.

GPs provide a convenient approach for modelling short time series.

This paper describes how existing bioenergy plants can be operated in order to offset fluctuations in power systems, performing a power system modelling based on time series data.

The estimate results validate that the employment of three types of non-Gaussian distribution is appropriate and essential for modelling wind power time series.

Based on (1), suppose the ARIMA modelling result for time series y t is: y_{t}^ = varphi_{1}^ y_{t - 1} + varepsilon_{t}^ (14)  From (2) we know that ɛ t is WGN, so ɛ t *  = 0.

In this paper, the authors apply the auto regressive time series modelling approach to produce spectral estimates of two such problems — non-stationary data obtained from the large amplitude response of a cable stayed bridge to wind excitation and non-linear data obtained from modal testing of cracked reinforced concrete beams.

Our modelling approach shows that applying the ARIMA time series models to forecast injury mortality in Xiamen is feasible.

We note that the graphical modelling approach presented here is not restricted to VAR models but could also be used for dynamic non-linear time series models very similar to the mixed models commonly used in the analysis of longitudinal data.

Uncertainty over time of assumptions means that if a model has a modelling horizon, and model results are displayed as time series, the related uncertainty of the results must increase towards the end of the horizon in future.

Uncertainty over time of such assumptions means that if a model has a modelling horizon,1 and model results are displayed as time series, the related uncertainty of the results must increase.

In order to check their adequacy, these methods are compared with other global and local modelling classical approaches using three benchmark time series and different sizes (medium and high) of training data sets.