Forecasting stock price by hybrid model of cascading Multivariate Adaptive Regression Splines and Deep Neural Network

2021 ◽  
Vol 95 ◽  
pp. 107405
Author(s):  
Ankita Bose ◽  
Ching-Hsien Hsu ◽  
Sanjiban Sekhar Roy ◽  
Kun Chang Lee ◽  
Behnam Mohammadi-ivatloo ◽  
...  
Keyword(s):  
Neural Network ◽  
Hybrid Model ◽  
Stock Price ◽  
Deep Neural Network ◽  
Multivariate Adaptive Regression Splines ◽  
Regression Splines ◽  
Adaptive Regression ◽  
Adaptive Regression Splines

scholarly journals ESTIMATING STRENGTH OF RUBBERIZED CONCRETE USING EVOLUTIONARY MULTIVARIATE ADAPTIVE REGRESSION SPLINES

2016 ◽  
Vol 22 (5) ◽  
pp. 711-720 ◽  
Author(s):  
Min-Yuan CHENG ◽  
Minh-Tu CAO
Keyword(s):  
Neural Network ◽  
Concrete Strength ◽  
Back Propagation ◽  
Multivariate Adaptive Regression Splines ◽  
Regression Splines ◽  
Rubberized Concrete ◽  
Comparison Results ◽  
Study Results ◽  
Adaptive Regression ◽  
Adaptive Regression Splines

This study proposes an artificial intelligence (AI) model to predict the compressive strength and splitting tensile strength of rubberized concrete. This Evolutionary Multivariate Adaptive Regression Splines (EMARS) model is a hybrid of the Multivariate Adaptive Regression Splines (MARS) and Artificial Bee Colony (ABC) within which MARS addresses learning and curve fitting and ABC implements optimization to determine the fittest parameter settings with minimal prediction error. K-fold cross validation was utilized to compare EMARS performance against four other benchmark data mining techniques including MARS, Back-propagation Neural Network (BPNN), Radial Basis Function Neural Network (RBFNN), and Genetic Programming (GP). Comparison results showed EMARS to be the best model for predicting rubberized concrete strength and study results demonstrated EMARS as a reliable tool for civil engineers in the concrete construction industry.

scholarly journals Evaluation of Multivariate Adaptive Regression Splines and Artificial Neural Network for Prediction of Mean Sea Level Trend around Northern Australian Coastlines

Mathematics ◽  
2021 ◽  
Vol 9 (21) ◽  
pp. 2696
Author(s):  
Nawin Raj ◽  
Zahra Gharineiat
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Sea Level ◽  
Multivariate Adaptive Regression Splines ◽  
Regression Splines ◽  
Ann Model ◽  
Mean Sea Level ◽  
Adaptive Regression ◽  
Artificial Neural ◽  
Adaptive Regression Splines

Mean sea level rise is a significant emerging risk from climate change. This research paper is based on the use of artificial intelligence models to assess and predict the trend on mean sea level around northern Australian coastlines. The study uses sea-level times series from four sites (Broom, Darwin, Cape Ferguson, Rosslyn Bay) to make the prediction. Multivariate adaptive regression splines (MARS) and artificial neural network (ANN) algorithms have been implemented to build the prediction model. Both models show high accuracy (R2 > 0.98) and low error values (RMSE < 27%) overall. The ANN model showed slightly better performance compared to MARS over the selected sites. The ANN performance was further assessed for modelling storm surges associated with cyclones. The model reproduced the surge profile with the maximum correlation coefficients ~0.99 and minimum RMS errors ~4 cm at selected validating sites. In addition, the ANN model predicted the maximum surge at Rosslyn Bay for cyclone Marcia to within 2 cm of the measured peak and the maximum surge at Broome for cyclone Narelle to within 7 cm of the measured peak. The results are comparable with a MARS model previously used in this region; however, the ANN shows better agreement with the measured peak and arrival time, although it suffers from slightly higher predictions than the observed sea level by tide gauge station.

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