scholarly journals Genetic Algorithm Based Improved ESTAR Nonlinear Models for Modelling Sunspot Numbers and Global Temperatures

2021 ◽  
Author(s):  
Bishal Gurung ◽  
Achal Lama ◽  
Santosha Rathod ◽  
K N Singh
Keyword(s):  
Genetic Algorithm ◽  
Nonlinear Models ◽  
Forecast Error ◽  
Optimization Procedure ◽  
Smooth Transition ◽  
Star Model ◽  
Estimation Of Parameters ◽  
Sunspot Numbers ◽  
Star Models ◽  
One Step

Abstract Smooth Transition Autoregressive (STAR) models are employed to describe cyclical data. As estimation of parameters of STAR using nonlinear methods was time-consuming, Genetic algorithm (GA), a powerful optimization procedure was applied for the same. Further, optimal one step and two step ahead forecasts along with their forecast error variances are derived theoretically for fitted STAR model using conditional expectations. Given the importance of the issue of global warming, the current paper aims to model the sunspot numbers and global mean temperatures. Further, appropriate tests are carried out to see if the model employed is appropriate for the datasets.

Forecasting Inflation Rate by Star Model: An Indian Experience

2002 ◽  
Vol 53 (3-4) ◽  
pp. 265-288
Author(s):  
G.P. Samanta
Keyword(s):  
Inflation Rate ◽  
Wholesale Price ◽  
Smooth Transition ◽  
Forecast Errors ◽  
Star Model ◽  
Star Models ◽  
Non Linear ◽  
Out Of Sample ◽  
Short Run ◽  
Forecasting Inflation

In this empirical study, an attempt has been made to model non-linear dynamics of inflation rate in India through Smooth Transition ⁄ Threshold Auto-Regression (STAR). Inflation is measured based on weekly data on Wholesale Price Index (WPI) fur a period of seven years from the week ended April 2, 1994 to the week ended March 31, 2001. The log(WPI) series is detected to be a Difference-Stationary process, indicating that the series is non-stationary but its first-order difference is stationary. The generating process of the transformed-stationary series is identified to be non-linear. Six variants of STAR model are estimated for transformed-stationary series and are used to forecast WPI and annual inflation rate. Empirical assessment of out-of-sample forecast errors eveals that estimated STAR models perform reasonably well in generating short-run forecasts of both the variables.

scholarly journals PERAMALAN HARGA SAHAM DENGAN METODE LOGISTIC SMOOTH TRANSITION AUTOREGRESSIVE (LSTAR) (Studi Kasus pada Harga Saham Mingguan PT. Bank Mandiri Tbk Periode 03 Januari 2011 sampai 24 Desember 2018)

2020 ◽  
Vol 9 (4) ◽  
pp. 391-401
Author(s):  
Maria Odelia ◽  
Di Asih I Maruddani ◽  
Hasbi Yasin
Keyword(s):  
Nonlinear Model ◽  
Stock Price ◽  
Nonlinear Models ◽  
Transition Function ◽  
Share Price ◽  
Smooth Transition ◽  
Star Model ◽  
Process Testing ◽  
Research Procedure ◽  
Smooth Transition Autoregressive

Series such as financial and economic data do not always form a linear model, so a nonlinear model is needed. One of the popular nonlinear models is the Smooth Transition Autoregressive (STAR). STAR has two possible suitable transition function such as logistic and exponential that need to be test to find the appropriate transition function. The purpose of writing this thesis is to determine the LSTAR model, then use the model to predict the stock price of PT Bank Mandiri. This study uses the data of the weekly stock price of PT Bank Mandiri from the period of January 3, 2011 to December 24, 2018 as insample data and the period of January 1, 2019 to December 30, 2019 as outsample data. The research procedure begins with modeling the data with the Autoregressive (AR) process, testing the linearity of the data, modeling with LSTAR, forecasting, and finally evaluating the results of forecasting. Evaluating the results of the forecasting of the weekly share price of PT Bank Mandiri with the STAR model results in the best nonlinear model LSTAR (1,1). This model produces an highly accurate forecasting result with a value of symmetric Mean Square Error (sMAPE) to be 5.12%.Keywords: Nonlinear, Time Series, STAR, LSTAR.

Modeling Multiple Regimes in the Business Cycle

1999 ◽  
Vol 3 (3) ◽  
pp. 311-340 ◽  
Author(s):  
Dick van Dijk ◽  
Philip Hans Franses
Keyword(s):  
Business Cycle ◽  
Growth Phase ◽  
High Growth ◽  
Smooth Transition ◽  
Star Model ◽  
Two Phase ◽  
Star Models ◽  
Moderate Growth ◽  
Multiple Regimes ◽  
The Business Cycle

The interest in business-cycle asymmetry has been steadily increasing over the past 15 years. Most research has focused on the different behavior of macroeconomic variables during expansions and contractions, which by now is well documented. Recent evidence suggests that such a two-phase characterization of the business cycle might be too restrictive. In particular, it might be worthwhile to decompose the recovery phase in a high-growth phase (immediately following the trough of a cycle) and a subsequent moderate-growth phase. The issue of multiple regimes in the business cycle is addressed using smooth-transition autoregressive (STAR) models. A possible limitation of STAR models as they currently are used is that essentially they deal with only two regimes. We propose a generalization of the STAR model such that more than two regimes can be accommodated. It is demonstrated that the class of multiple-regime STAR (MRSTAR) models can be obtained from the two-regime model in a simple way. The main properties of the MRSTAR model and several issues that are relevant for empirical specification are discussed in detail. In particular, a Lagrange multiplier-type test is derived that can be used to determine the appropriate number of regimes. A limited simulation study indicates its practical usefulness. Application of the new model class to U.S. real GNP provides evidence in favor of the existence of multiple business-cycle phases.

scholarly journals Testing Market Efficiency with Nonlinear Methods: Evidence from Borsa Istanbul

2019 ◽  
Vol 7 (2) ◽  
pp. 27
Author(s):  
Fuzuli Aliyev
Keyword(s):  
Random Walk ◽  
Market Efficiency ◽  
Financial Time Series ◽  
Weak Form ◽  
Ar Model ◽  
Smooth Transition ◽  
Star Model ◽  
Forecast Performance ◽  
Star Models ◽  
Out Of Sample

Market efficiency has been analyzed through many studies using different linear methods. However, studies on financial econometrics reveal that financial time series exhibit nonlinear patterns because of various reasons. This paper examines market efficiency at Borsa Istanbul using a smooth transition autoregressive (STAR) type nonlinear model. I develop nonlinear ARCH and STAR models, a linear AR model and random walk model for 10 years’ weekly data and then out-of-sample forecast next 12 weeks’ return. Comparing forecast performance powers, I find that the STAR model outperforms random walk, that is Borsa Istanbul returns are predictable at the given period. The results show that the shareholders may earn abnormal return and identify the direction of the return change for the next week with at least 66% accuracy. Contrary to the linear level studies, these findings show that the Borsa Istanbul is not weak form efficient at nonlinear level within the studied period.

scholarly journals Hybrid Optimization Based Mathematical Procedure for Dimensional Synthesis of Slider-Crank Linkage

Mathematics ◽  
2021 ◽  
Vol 9 (13) ◽  
pp. 1581
Author(s):  
Alfonso Hernández ◽  
Aitor Muñoyerro ◽  
Mónica Urízar ◽  
Enrique Amezua
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Linear Equations ◽  
Optimization Procedure ◽  
Optimization Techniques ◽  
Dimensional Synthesis ◽  
Hybrid Strategy ◽  
Dimensional Parameters ◽  
Optimization Methodology ◽  
Crank Mechanism

In this paper, an optimization procedure for path generation synthesis of the slider-crank mechanism will be presented. The proposed approach is based on a hybrid strategy, mixing local and global optimization techniques. Regarding the local optimization scheme, based on the null gradient condition, a novel methodology to solve the resulting non-linear equations is developed. The solving procedure consists of decoupling two subsystems of equations which can be solved separately and following an iterative process. In relation to the global technique, a multi-start method based on a genetic algorithm is implemented. The fitness function incorporated in the genetic algorithm will take as arguments the set of dimensional parameters of the slider-crank mechanism. Several illustrative examples will prove the validity of the proposed optimization methodology, in some cases achieving an even better result compared to mechanisms with a higher number of dimensional parameters, such as the four-bar mechanism or the Watt’s mechanism.

Optimization of Turbofan Propulsion Cycle Using a Genetic Algorithm

2010 ◽  
Author(s):  
Adel Ghenaiet
Keyword(s):  
Genetic Algorithm ◽  
Fuel Consumption ◽  
Pressure Ratio ◽  
Engine Performance ◽  
Optimization Procedure ◽  
Specific Fuel Consumption ◽  
High Mass ◽  
Design Parameters ◽  
Inlet Temperature ◽  
Simplifying Assumptions

This paper presents an evolutionary approach as the optimization framework to design for the optimal performance of a high-bypass unmixed turbofan to match with the power requirements of a commercial aircraft. The parametric analysis had the objective to highlight the effects of the principal design parameters on the propulsive performance in terms of specific fuel consumption and specific thrust. The design optimization procedure based on the genetic algorithm PIKAIA coupled to the developed engine performance analyzer (on-design and off-design) aimed at finding the propulsion cycle parameters minimizing the specific fuel consumption, while meeting the required thrusts in cruise and takeoff and the restrictions of temperatures limits, engine size and weight as well as pollutants emissions. This methodology does not use engine components’ maps and operates on simplifying assumptions which are satisfying the conceptual or early design stages. The predefined requirements and design constraints have resulted in an engine with high mass flow rate, bypass ratio and overall pressure ratio and a moderate turbine inlet temperature. In general, the optimized engine is fairly comparable with available engines of equivalent power range.

Structural Optimization of Composite Panel with Lamination Parameters and Equivalent Stiffness Laminate Method

2014 ◽  
Vol 496-500 ◽  
pp. 429-435
Author(s):  
Xiao Ping Zhong ◽  
Peng Jin
Keyword(s):  
Genetic Algorithm ◽  
Structural Optimization ◽  
Composite Structure ◽  
Optimization Procedure ◽  
Optimization Method ◽  
Composite Panel ◽  
Analysis Tool ◽  
Equivalent Stiffness ◽  
Lamination Parameters ◽  
Design Variables

Firstly, a two-level optimization procedure for composite structure is investigated with lamination parameters as design variables and MSC.Nastran as analysis tool. The details using lamination parameters as MSC.Nastran input parameters are presented. Secondly, with a proper equivalent stiffness laminate built to substitute for the lamination parameters, a two-level optimization method based on the equivalent stiffness laminate is proposed. Compared with the lamination parameters-based method, the layer thicknesses of the equivalent stiffness laminate are adopted as continuous design variables at the first level. The corresponding lamination parameters are calculated from the optimal layer thicknesses. At the second level, genetic algorithm (GA) is applied to identify an optimal laminate configuration to target the lamination parameters obtained. The numerical example shows that the proposed method without considering constraints of lamination parameters can obtain better optimal results.

scholarly journals Dynamically Changing Cattle Market Linkages with Supply-Side-Controlled Transitions

2019 ◽  
Vol 51 (3) ◽  
pp. 472-484
Author(s):  
Wenying Li ◽  
Yunhan Li ◽  
Jeffrey H. Dorfman
Keyword(s):  
Simple Procedure ◽  
Economic Indicator ◽  
The United States ◽  
Transition Function ◽  
Smooth Transition ◽  
Supply Side ◽  
Star Model ◽  
Cattle Markets ◽  
Cattle Market ◽  
Smooth Transition Autoregressive

AbstractCattle are costly to transport, which could lead to segmented regional cattle markets. The cointegration of cattle prices over regions has been of research interest for decades. This article investigates price cointegration between regional cattle markets in the United States and proposes a simple procedure for incorporating a flexible transition function into an economic indicator–controlled smooth transition autoregressive (ECON-STAR) model to evaluate market dynamics. The empirical results show that these markets have been highly integrated when excess supply exists, but when cattle inventories decrease, the market pattern becomes very regionally segmented.

Design improvement and fatigue analysis for a bicycle handlebar stem system using uniform design method and genetic algorithm

2020 ◽  
Vol 234 (11) ◽  
pp. 2266-2278
Author(s):  
Cho-Pei Jiang ◽  
Ching-Wei Wu ◽  
Yung-Chang Cheng
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Safety Factor ◽  
Uniform Design ◽  
Optimization Procedure ◽  
Bending Test ◽  
Von Mises Stress ◽  
Kriging Interpolation ◽  
Optimized Design ◽  
Von Mises

An integrating optimization procedure is presented to improve the von Mises stress and fatigue safety factor for a handlebar stem system in a bicycle system. The optimization procedure involves uniform design of experiment, Kriging interpolation, genetic algorithm, and nonlinear programming method. Using ANSYS/Workbench software and the ISO 4210 bicycle handlebar stem testing standard, the von Mises stress for the lateral bending test simulation and the fatigue safety factor for the fatigue test simulation is calculated. The von Mises stress and fatigue safety factor are combined into a single and integrated objective function, and Kriging interpolation is then used to create the surrogate model of the integrated objective function. When the integrating optimization procedure is used, the integrated objective function demonstrates that the von Mises stress for the optimized handlebar stem is reduced to 225 MPa and the fatigue safety factor increases to 1.796. This shows that the optimized design increases the strength of the handlebar stem. The proposed technique yields a handlebar stem with an optimized shape.

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