Genetic programming in time series modelling: an application to meteorological data

2002 ◽  
Author(s):  
K.R. Vazquez
Keyword(s):  
Time Series ◽  
Genetic Programming ◽  
Meteorological Data ◽  
Time Series Modelling

Time-Series Modelling of Avalanche Activity from Meteorological Data

1979 ◽  
Vol 22 (88) ◽  
pp. 513-528 ◽  
Author(s):  
A. A. Salway
Keyword(s):  
Time Series ◽  
Correlation Analysis ◽  
Meteorological Data ◽  
Weighting Scheme ◽  
Hazard Evaluation ◽  
Linear Discriminant ◽  
Multivariate Technique ◽  
Time Series Modelling ◽  
Time Series Approach ◽  
Time Lagged

AbstractAvalanche-hazard evaluation by field analysts is largely achieved along causal intuitive lines, depending for its success upon the experience of the analyst in his particular area. Several attempts have been made in the past to quantify such procedures by means of statistical models based upon meteorological measurements. Modified forms of a multivariate technique known as linear discriminant analysis have been tried with only partial success. Intercorrelated variables and autocorrelated data, omission of time-lagged terms, insufficient variation in the dependent variable, and sampling difficulties may have combined to weaken the discriminant approach. These problems and the nature of the phenomenon suggest that a stochastic transfer-function time-series approach may be a useful alternative method.A numerical weighting scheme has been devised for the representation of avalanche activity for the Rogers Pass area of British Columbia in terms of terminus, size, and moisture-content codes for each event. From various types of correlation analysis performed on data for the period 1965–73, models were developed using the “best” weighting scheme for avalanche activity representation and the most promising meteorological variables, as indicated by the results of the correlation analysis.These relatively simple models demonstrate a good fit to the actual data, in both a descriptive and a simulated-forecasting situation.

scholarly journals Time-Series Modelling of Avalanche Activity from Meteorological Data

1979 ◽  
Vol 22 (88) ◽  
pp. 513-528
Author(s):  
A. A. Salway
Keyword(s):  
Time Series ◽  
Correlation Analysis ◽  
Meteorological Data ◽  
Weighting Scheme ◽  
Hazard Evaluation ◽  
Linear Discriminant ◽  
Multivariate Technique ◽  
Time Series Modelling ◽  
Time Series Approach ◽  
Time Lagged

AbstractAvalanche-hazard evaluation by field analysts is largely achieved along causal intuitive lines, depending for its success upon the experience of the analyst in his particular area. Several attempts have been made in the past to quantify such procedures by means of statistical models based upon meteorological measurements. Modified forms of a multivariate technique known as linear discriminant analysis have been tried with only partial success. Intercorrelated variables and autocorrelated data, omission of time-lagged terms, insufficient variation in the dependent variable, and sampling difficulties may have combined to weaken the discriminant approach. These problems and the nature of the phenomenon suggest that a stochastic transfer-function time-series approach may be a useful alternative method.A numerical weighting scheme has been devised for the representation of avalanche activity for the Rogers Pass area of British Columbia in terms of terminus, size, and moisture-content codes for each event. From various types of correlation analysis performed on data for the period 1965–73, models were developed using the “best” weighting scheme for avalanche activity representation and the most promising meteorological variables, as indicated by the results of the correlation analysis.These relatively simple models demonstrate a good fit to the actual data, in both a descriptive and a simulated-forecasting situation.

scholarly journals Bernoulli Time Series Modelling with Application to Accommodation Tourism Demand

2021 ◽  
Vol 5 (1) ◽  
pp. 17
Author(s):  
Miguel Ángel Ruiz Reina
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Prognostic Models ◽  
Series Data ◽  
Economic Agents ◽  
Time Series Modelling ◽  
Simulation And Training ◽  
Uncertainty Method ◽  
And Training ◽  
Hotel Accommodation

In this research, a new uncertainty method has been developed and applied to forecasting the hotel accommodation market. The simulation and training of Time Series data are from January 2001 to December 2018 in the Spanish case. The Log-log BeTSUF method estimated by GMM-HAC-Newey-West is considered as a contribution for measuring uncertainty vs. other prognostic models in the literature. The results of our model present better indicators of the RMSE and Ratio Theil’s for the predictive evaluation period of twelve months. Furthermore, the straightforward interpretation of the model and the high descriptive capacity of the model allow economic agents to make efficient decisions.

scholarly journals Time-series modelling and forecasting of hand, foot and mouth disease cases in China from 2008 to 2018

2019 ◽  
Vol 147 ◽  
Author(s):  
C. W. Tian ◽  
H. Wang ◽  
X. M. Luo
Keyword(s):  
Time Series ◽  
Seasonal Variation ◽  
Moving Average ◽  
Foot And Mouth Disease ◽  
Mouth Disease ◽  
Sarima Model ◽  
Autoregressive Integrated Moving Average ◽  
Annual Peak ◽  
Time Series Modelling ◽  
Foot And Mouth

AbstractSeasonal autoregressive-integrated moving average (SARIMA) has been widely used to model and forecast incidence of infectious diseases in time-series analysis. This study aimed to model and forecast monthly cases of hand, foot and mouth disease (HFMD) in China. Monthly incidence HFMD cases in China from May 2008 to August 2018 were analysed with the SARIMA model. A seasonal variation of HFMD incidence was found from May 2008 to August 2018 in China, with a predominant peak from April to July and a trough from January to March. In addition, the annual peak occurred periodically with a large annual peak followed by a relatively small annual peak. A SARIMA model of SARIMA (1, 1, 2) (0, 1, 1)12 was identified, and the mean error rate and determination coefficient were 16.86% and 94.27%, respectively. There was an annual periodicity and seasonal variation of HFMD incidence in China, which could be predicted well by a SARIMA (1, 1, 2) (0, 1, 1)12 model.

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