Formulation of A Sale Price Prediction Model Based on Fuzzy Regression Analysis

2012 ◽  
pp. 567-572 ◽  
Author(s):  
Michihiro Amagasa
Keyword(s):  
Regression Analysis ◽  
Prediction Model ◽  
Fuzzy Regression ◽  
Sale Price ◽  
Price Prediction ◽  
Fuzzy Regression Analysis ◽  
Model Based

Sale Price Prediction Model including Maintenance Factors in Enterprise-type Rental Housing

2016 ◽  
Vol 3 (1) ◽  
pp. 1-10
Author(s):  
InSeok Park ◽  
◽  
ZhengXun Jin ◽  
HyunBin Kim ◽  
JongHyeob Kim ◽  
...  
Keyword(s):  
Prediction Model ◽  
Rental Housing ◽  
Sale Price ◽  
Price Prediction ◽  
Maintenance Factors

scholarly journals A Carbon Price Prediction Model Based on the Secondary Decomposition Algorithm and Influencing Factors

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1328
Author(s):  
Jianguo Zhou ◽  
Shiguo Wang
Keyword(s):  
Prediction Model ◽  
Extreme Learning Machine ◽  
Influencing Factors ◽  
Search Algorithm ◽  
Carbon Price ◽  
Carbon Trading ◽  
Price Prediction ◽  
Model Based ◽  
Mode Decomposition ◽  
Learning Machine

Carbon emission reduction is now a global issue, and the prediction of carbon trading market prices is an important means of reducing emissions. This paper innovatively proposes a second decomposition carbon price prediction model based on the nuclear extreme learning machine optimized by the Sparrow search algorithm and considers the structural and nonstructural influencing factors in the model. Firstly, empirical mode decomposition (EMD) is used to decompose the carbon price data and variational mode decomposition (VMD) is used to decompose Intrinsic Mode Function 1 (IMF1), and the decomposition of carbon prices is used as part of the input of the prediction model. Then, a maximum correlation minimum redundancy algorithm (mRMR) is used to preprocess the structural and nonstructural factors as another part of the input of the prediction model. After the Sparrow search algorithm (SSA) optimizes the relevant parameters of Extreme Learning Machine with Kernel (KELM), the model is used for prediction. Finally, in the empirical study, this paper selects two typical carbon trading markets in China for analysis. In the Guangdong and Hubei markets, the EMD-VMD-SSA-KELM model is superior to other models. It shows that this model has good robustness and validity.

scholarly journals Development of a Survival Model Based on Autophagy-Associated Genes for Predicting Prognosis of Gastric Cancer

2020 ◽  
Author(s):  
Wanli Yang ◽  
Lili Duan ◽  
Xinhui Zhao ◽  
Liaoran Niu ◽  
Yiding Li ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Regression Analysis ◽  
Prediction Model ◽  
Roc Curve ◽  
Survival Model ◽  
Differentially Expressed ◽  
Lasso Regression ◽  
Prognosis Prediction ◽  
Cellular Processes ◽  
Model Based

Abstract Background: Gastric cancer (GC) is one of lethal diseases worldwide. Autophagy-associated genes play a crucial role in the cellular processes of GC. Our study aimed to investigate and identify the prognostic potential of autophagy-associated genes signature in GC. Methods: RNA-seq and clinical information of GC and normal controls were downloaded from The Cancer Genome Atlas (TCGA) database. Then, the Wilcoxon signed-rank test was used to pick out the differentially expressed autophagy-associated genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to investigate the potential roles and mechanisms of autophagy-associated genes in GC. Cox proportional hazard regression analysis and Lasso regression analysis were carried out to identify the overall survival (OS) related autophagy-associated genes, which were then collected to construct a predictive model. Kaplan-Meier method and receiver operating characteristic (ROC) curve were utilized to validate the accuracy of this model. Finally, a clinical nomogram was established by combining the clinical factors and autophagy-associated genes signature. Results: A total of 28 differentially expressed autophagy-associated genes were identified. GO and KEGG analyses revealed that several important cellular processes and signaling pathways were correlated with these genes. Through Cox regression and Lasso regression analyses, we identified 4 OS-related autophagy-associated genes (GRID2, ATG4D, GABARAPL2, and CXCR4) and constructed a prognosis prediction model. GC Patients with high-risk had a worse OS than those in low-risk group (5-year OS, 27.7% vs 38.3%; P=9.524e-07). The area under the ROC curve (AUC) of the prediction model was 0.67. The nomogram was demonstrated to perform better for predicting 3-year and 5-year survival possibility for GC patients with a concordance index (C-index) of 0.70 (95% CI: 0.65-0.72). The calibration curves also presented good concordance between nomogram-predicted survival and actual survival. Conclusions: We constructed and evaluated a survival model based on the autophagy-associated genes for GC patients, which may improve the prognosis prediction in GC.

Robust fuzzy regression analysis

2011 ◽  
Vol 181 (19) ◽  
pp. 4154-4174 ◽  
Author(s):  
Pierpaolo D’Urso ◽  
Riccardo Massari ◽  
Adriana Santoro
Keyword(s):  
Regression Analysis ◽  
Fuzzy Regression ◽  
Fuzzy Regression Analysis
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