scholarly journals Long-Term Prediction of Vitreous Materials Degradation Derived from Short-Term Testing

2020 ◽  
Author(s):  
Albert Kruger
Keyword(s):  
Short Term ◽  
Term Prediction ◽  
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.

scholarly journals Novel Robust Time Series Analysis for Long-term and Short-term Prediction

2021 ◽  
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

Abstract Nonlinear 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 traditional least squares and least absolute deviations methods 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 the other methods fail to estimate autocorrelation accurately.

scholarly journals Empirical regional models for the short-term forecast of <i>M3000F2</i> during not quiet geomagnetic conditions over Europe

2013 ◽  
Vol 31 (10) ◽  
pp. 1653-1671 ◽  
Author(s):  
M. Pietrella
Keyword(s):  
Correction Factor ◽  
Regional Model ◽  
Local Models ◽  
Short Term ◽  
European Area ◽  
Term Prediction ◽  
Starting Point ◽  
Short Term Forecasting ◽  
Long Term Prediction

Abstract. Twelve empirical local models have been developed for the long-term prediction of the ionospheric characteristic M3000F2, and then used as starting point for the development of a short-term forecasting empirical regional model of M3000F2 under not quiet geomagnetic conditions. Under the assumption that the monthly median measurements of M3000F2 are linearly correlated to the solar activity, a set of regression coefficients were calculated over 12 months and 24 h for each of 12 ionospheric observatories located in the European area, and then used for the long-term prediction of M3000F2 at each station under consideration. Based on the 12 long-term prediction empirical local models of M3000F2, an empirical regional model for the prediction of the monthly median field of M3000F2 over Europe (indicated as RM_M3000F2) was developed. Thanks to the IFELM_foF2 models, which are able to provide short-term forecasts of the critical frequency of the F2 layer (foF2STF) up to three hours in advance, it was possible to considerer the Brudley–Dudeney algorithm as a function of foF2STF to correct RM_M3000F2 and thus obtain an empirical regional model for the short-term forecasting of M3000F2 (indicated as RM_M3000F2_BD) up to three hours in advance under not quiet geomagnetic conditions. From the long-term predictions of M3000F2 provided by the IRI model, an empirical regional model for the forecast of the monthly median field of M3000F2 over Europe (indicated as IRI_RM_M3000F2) was derived. IRI_RM_M3000F2 predictions were modified with the Bradley–Dudeney correction factor, and another empirical regional model for the short-term forecasting of M3000F2 (indicated as IRI_RM_M3000F2_BD) up to three hours ahead under not quiet geomagnetic conditions was obtained. The main results achieved comparing the performance of RM_M3000F2, RM_M3000F2_BD, IRI_RM_M3000F2, and IRI_RM_M3000F2_BD are (1) in the case of moderate geomagnetic activity, the Bradley–Dudeney correction factor does not improve significantly the predictions; (2) under disturbed geomagnetic conditions, the Bradley–Dudeney formula improves the predictions of RM_M3000F2 in the entire European area; (3) in the case of very disturbed geomagnetic conditions, the Bradley–Dudeney algorithm is very effective in improving the performance of IRI_RM_M3000F2; (4) under moderate geomagnetic conditions, the long-term prediction maps of M3000F2 generated by RM_M3000F2 can be considered as short-term forecasting maps providing very satisfactory results because quiet geomagnetic conditions are not so diverse from moderate geomagnetic conditions; (5) the forecasting maps originated by RM_M3000F2, RM_M3000F2_BD, and IRI_RM_M3000F2_BD show some regions where the forecasts are not satisfactory, but also wide sectors where the M3000F2 forecasts quite faithfully match the M3000F2 observations, and therefore RM_M3000F2, RM_M3000F2_BD, and IRI_RM_M3000F2_BD could be exploited to produce short-term forecasting maps of M3000F2 over Europe up to 3 h in advance.

A climate-sensitive transition matrix growth model for uneven-aged mixed-species oak forests in North China

2020 ◽  
Author(s):  
Xue Du ◽  
Xinyun Chen ◽  
Weisheng Zeng ◽  
Jinghui Meng
Keyword(s):  
Climate Change ◽  
Growth Model ◽  
Transition Matrix ◽  
Forest Growth ◽  
Oak Forests ◽  
Mixed Species ◽  
Short Term ◽  
Term Prediction ◽  
Long Term Prediction

Abstract Oak-dominated forests, economically and ecologically valuable ecosystems, are widely distributed in China. These oak-dominated forests are now generally degraded coppice forests, and are of relatively low quality. Climate change has been shown to affect forest growth, tree mortality, and recruitment, but available forest growth models are lacking to study climate effects. In this study, a climate-sensitive, transition-matrix growth model (CM) was developed for uneven-aged, mixed-species oak forests using data collected from 253 sample plots from the 8th (2010) and 9th (2015) Chinese National Forest Inventory in Shanxi Province, China. To investigate robustness of the model, we also produced a variable transition model that did not consider climate change (NCM), and fixed parameter transition matrix model (FM), using the same data. Short-term and long-term predictive performance of CM, NCM, and FM were compared. Results indicated that for short-term prediction (5 years), there was almost no significant difference among the three predictive models, though CM exhibited slightly better performance. In contrast, for long-term prediction (100 years), CM, under the three representative concentration pathways (RCPs), i.e. RCP2.6, RCP4.5 and RCP8.5, indicated rather different dynamics that were more reliable because climate factors were considered which could significantly influence forest dynamics, especially in long-term prediction intervals. The CM model provides a framework for the management of mixed-species oak forests in the context of climate change.

scholarly journals Ship Model Identification with Genetic Algorithm Tuning

2021 ◽  
Vol 11 (12) ◽  
pp. 5504
Author(s):  
Anna Miller
Keyword(s):  
Genetic Algorithm ◽  
Standard Deviation ◽  
Short Term ◽  
Validation Data ◽  
Ship Model ◽  
Ship Dynamics ◽  
Term Prediction ◽  
Predictive Controller ◽  
Long Term Prediction

Modeling is the most important component in predictive controller design. It should predict outputs precisely and fast. Thus, it must be adequate for the ship dynamics while having as simple a structure as possible. In a good ship model the standard deviation of a particular coefficient should not exceed 10% of its value. Fitting the validation data to 80% for short-term prediction and 65% for long-term prediction is treated as a declared benchmark for model usage in ship course predictive controller. Regularization was proposed to ensure better state-space models to fit the real ship dynamics and more accurate standard deviation value control. Usage of the simulation results and real-time trials, as model estimation and validation data, respectively, during the identification procedure is proposed. In the first step a predictive linear model is identified conventionally, and then coefficients are regularized, based on the validation data, using a genetic algorithm. Particular linearized model coefficient standard deviations were decreased from more than 100% of their values to approximately 5% of them using genetic algorithm tuning. Moreover, the proposed method eliminated model output signal oscillations, which were observed during the validation process based on experimental data, gained during ship trials. Improved mapping of ship dynamics was achieved. Fit to validation data increased from 71% and 54% to 89% and 76%, respectively, for short-term and long-term prediction. The proposed method, which may be applied to real applications, is easily applicable and reliable. The tuned model is sufficiently suited to plant dynamics and may be used for future predictive control purposes.

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