Wave loads Long-term prediction of combined wave and whipping bending moments of containership

2013 ◽  
pp. 15-22 ◽  
Keyword(s):  
Bending Moments ◽  
Wave Loads ◽  
Term Prediction ◽  
Long Term Prediction

Long-term prediction of combined wave and whipping bending moments of container ships

2013 ◽  
Vol 10 (1) ◽  
pp. 4-19 ◽  
Author(s):  
Maro Ćorak ◽  
Joško Parunov ◽  
Carlos Guedes Soares
Keyword(s):  
Bending Moments ◽  
Term Prediction ◽  
Long Term Prediction

Long term prediction of non-linear vertical bending moments on a fast monohull

2004 ◽  
Vol 26 (6) ◽  
pp. 288-297 ◽  
Author(s):  
C. Guedes Soares ◽  
N. Fonseca ◽  
R. Pascoal
Keyword(s):  
Bending Moments ◽  
Term Prediction ◽  
Non Linear ◽  
Long Term Prediction

scholarly journals Novel robust time series analysis for long-term and short-term prediction

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hiroshi Okamura ◽  
Yutaka Osada ◽  
Shota Nishijima ◽  
Shinto Eguchi
Keyword(s):  
Robust Regression ◽  
Simulated Data ◽  
New Method ◽  
Short Term ◽  
Least Absolute Deviations ◽  
Term Prediction ◽  
Regression Approach ◽  
Short Term Prediction ◽  
Long Term Prediction

AbstractNonlinear phenomena are universal in ecology. However, their inference and prediction are generally difficult because of autocorrelation and outliers. A traditional least squares method for parameter estimation is capable of improving short-term prediction by estimating autocorrelation, whereas it has weakness to outliers and consequently worse long-term prediction. In contrast, a traditional robust regression approach, such as the least absolute deviations method, alleviates the influence of outliers and has potentially better long-term prediction, whereas it makes accurately estimating autocorrelation difficult and possibly leads to worse short-term prediction. We propose a new robust regression approach that estimates autocorrelation accurately and reduces the influence of outliers. We then compare the new method with the conventional least squares and least absolute deviations methods by using simulated data and real ecological data. Simulations and analysis of real data demonstrate that the new method generally has better long-term and short-term prediction ability for nonlinear estimation problems using spawner–recruitment data. The new method provides nearly unbiased autocorrelation even for highly contaminated simulated data with extreme outliers, whereas other methods fail to estimate autocorrelation accurately.

Long term prediction of forest resources

1985 ◽  
Vol 12 ◽  
pp. 176
Author(s):  
H Kurth ◽  
B Anders ◽  
G Lucas
Keyword(s):  
Forest Resources ◽  
Term Prediction ◽  
Long Term Prediction
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