Granular Computing-Based Long-Term Prediction Intervals

2018 ◽  
pp. 223-267
Author(s):  
Jun Zhao ◽  
Wei Wang ◽  
Chunyang Sheng
Keyword(s):  
Granular Computing ◽  
Prediction Intervals ◽  
Term Prediction ◽  
Long Term Prediction

scholarly journals Granular Computing Concept based long-term prediction of Gas Tank Levels in Steel Industry

2014 ◽  
Vol 47 (3) ◽  
pp. 6105-6110 ◽  
Author(s):  
Zhongyang Han ◽  
Jun Zhao ◽  
Wei Wang ◽  
Ying Liu ◽  
Quanli Liu
Keyword(s):  
Steel Industry ◽  
Granular Computing ◽  
Term Prediction ◽  
Long Term Prediction

scholarly journals Long-term prediction intervals of economic time series

2019 ◽  
Vol 58 (1) ◽  
pp. 191-222
Author(s):  
M. Chudý ◽  
S. Karmakar ◽  
W. B. Wu
Keyword(s):  
Time Series ◽  
Prediction Intervals ◽  
Economic Time Series ◽  
Term Prediction ◽  
Economic Time ◽  
Long Term Prediction

Long-Term Prediction Intervals of Time Series

2010 ◽  
Vol 56 (3) ◽  
pp. 1436-1446 ◽  
Author(s):  
Zhou Zhou ◽  
Zhiwei Xu ◽  
Wei Biao Wu
Keyword(s):  
Time Series ◽  
Prediction Intervals ◽  
Term Prediction ◽  
Long Term Prediction

scholarly journals Long‐term prediction intervals with many covariates

2021 ◽  
Author(s):  
Sayar Karmakar ◽  
Marek Chudý ◽  
Wei Biao Wu
Keyword(s):  
Prediction Intervals ◽  
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.

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