scholarly journals Regresi Nonparametrik Kernel Gaussian pada Pemodelan Angka Kelahiran Kasar di Provinsi Nusa Tenggara Barat

2020 ◽  
Vol 3 (2) ◽  
pp. 100
Author(s):  
Deni Pratiwi ◽  
Lalu Abd Azis Mursy ◽  
Muhammad Rizaldi ◽  
Nurul Fitriyani
Keyword(s):  
Nonparametric Regression ◽  
Cross Validation ◽  
Simulated Annealing Algorithm ◽  
Gaussian Function ◽  
Distribution Patterns ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
A Value ◽  
The Mean ◽  
Annealing Algorithm

This study aims to model Crude Birth Rates (CBR) in West Nusa Tenggara Province. The nonparametric regression method was used in this research by considering data distribution patterns that do not show a linear relationship between variables. In this case, the kernel nonparametric regression using the Gaussian function and the Nadaraya-Watson estimator. The results showed optimal bandwidths of 0.55542837, 1.29042927, 0.94706041, and 0.92278896 with a value of minimum Generalized Cross-Validation (GCV) of 0.000000000432613511, which was minimized by the simulated annealing algorithm. The resulting model's accuracy can be seen from the coefficient of determination (R2) of 99.23% and the Mean Absolute Percentage Error (MAPE) of 0.007049%.

scholarly journals Model Regresi Nonparametrik dengan Pendekatan Spline (Studi Kasus: Berat Badan Lahir Rendah di Rumah Sakit Ibu dan Anak Siti Fatimah Makassar)

2020 ◽  
Vol 2 (1) ◽  
pp. 70
Author(s):  
Wahidah Sanusi ◽  
Rahmat Syam ◽  
Rabiatul Adawiyah
Keyword(s):  
Birth Weight ◽  
Low Birth Weight ◽  
Nonparametric Regression ◽  
Gestational Age ◽  
Cross Validation ◽  
Predictor Variable ◽  
Coefficient Of Determination ◽  
Generalized Cross Validation ◽  
Child Hospital ◽  
The Relationship

Pendekatan nonparametrik merupakan suatu pendekatan yang digunakan apabila bentuk hubungan antara variabel respon dan variabel prediktornya tidak diketahui atau tidak adanya informasi mengenai bentuk fungsi regresinya. Spline merupakan suatu teknik yang dilakukan untuk mengestimasi parameter dalam regresi nonparametrik. Penelitian ini bertujuan untuk mengetahui model hubungan antara berat badan lahir rendah dan faktor-faktor yang mempengaruhi berdasarkan model spline. Faktor-faktor tersebut adalah usia ibu, usia kehamilan, dan jarak kehamilan. Data tersebut diperoleh dari rumah sakit ibu dan anak siti Fatimah Makassar tahun 2017. Dimana untuk mendapatkan model spline terbaik langkah awal yang dilakukan adalah menentukan knot dengan nilai Generalized Cross Validation (GCV) yang minimum. Berdasarkan penelitian yang telah dilakukan, dua variabel dinyatakan berpengaruh terhadap berat badan lahir rendah yaitu usia ibu, dan usia kehamilan. Model regresi nonparametrik dengan pendekatan Spline yang terbentuk memiliki koefisien determinasi sebesar 78,19%, serta nilai GCV dengan tiga titik knot yaitu 0.0117.Kata kunci: Regresi Nonparametrik, Spline, Berat Badan Lahir Rendah, Generalized Cross Validation The non-parametric approach is an approach that is used if the form of the relationship between the response variable and the predictor variable is unknown or the absence of information about the shapes of regression functions. The Spline is a technique performed to estimate the parameters in the nonparametric regression. This study aims to determine the model of the relationship between low birth weight and the factors that affect the based on the spline model. Such factors are maternal age, gestational age, and pregnancy distance. The Data is obtained from the mother and child hospital siti Fatimah Makassar 2017. Where to get a spline model best the initial step is to determine the knots with the value of the Generalized Cross Validation (GCV) which is a minimum. Based on the research that has been done, the two variables stated effect against low birth weight, namely age of mother, and gestational age. Nonparametric regression Model with the approach of the Spline that is formed has a coefficient of determination of 78.19 to%, as well as the value of the GCV with a three-point knot that is 0.0117.Keyword : Nonparametric Regression, Spline, Low Birth Weight, Generalized Cross Validation

A Neural Network for Coding of Trajectories by Time Series of Neuronal Population Vectors

1994 ◽  
Vol 6 (1) ◽  
pp. 19-28 ◽  
Author(s):  
Alexander V. Lukashin ◽  
Apostolos P. Georgopoulos
Keyword(s):  
Neural Network ◽  
Motor Cortex ◽  
Simulated Annealing Algorithm ◽  
Neuronal Population ◽  
Population Vector ◽  
Feedback Neural Network ◽  
Neuronal Pair ◽  
The Mean ◽  
Annealing Algorithm ◽  
Experimental Findings

The neuronal population vector is a measure of the combined directional tendency of the ensemble of directionally tuned cells in the motor cortex. It has been found experimentally that a trajectory of limb movement can be predicted by adding together population vectors, tip-to-tail, calculated for successive instants of time to construct a neural trajectory. In the present paper we consider a model of the dynamic evolution of the population vector. The simulated annealing algorithm was used to adjust the connection strengths of a feedback neural network so that it would generate a given trajectory by a sequence of population vectors. This was repeated for different trajectories. Resulting sets of connection strengths reveal a common feature regardless of the type of trajectories generated by the network: namely, the mean connection strength was negatively correlated with the angle between the preferred directions of neuronal pair involved in the connection. The results are discussed in the light of recent experimental findings concerning neuronal connectivity within the motor cortex.

Improved Grey Forecasting Model for Taiwans Green Accounting

2013 ◽  
Vol 404 ◽  
pp. 398-403 ◽  
Author(s):  
Ching I Lin ◽  
Shin Li Lu ◽  
Shih Hung Tai
Keyword(s):  
Prediction Accuracy ◽  
Economic Indicator ◽  
Percentage Error ◽  
Forecasting Model ◽  
Environmental Costs ◽  
Green Accounting ◽  
Grey Forecasting ◽  
A Value ◽  
Grey Forecasting Model ◽  
The Mean

This paper applies the grey forecasting model to forecast the green accounting of Taiwan from 2002 to 2010. Green accounting is an effective economic indicator of human environmental and natural resources protection. Generally, Green accounting is a type of accounting that attempts to factor environmental costs into the financial results of operations. This paper modifies the original GM(1,1) model to improve prediction accuracy in green accounting and also provide a value reference for government in drafting relevant economic and environmental policies. Empirical study shows that the mean absolute percentage error of RGM(1,1) model is 2.05% lower than GM(1,1) and AGM(1,1), respectively. Results are very encouraging as the RGM(1,1) forecasting model clearly enhances the prediction accuracy.

scholarly journals Analyzing The Effect of BI 7-Days Repo Rate on The Jakarta Composite Index Using Nonparametric Regression Approaches Based on Least Square Spline Estimator

2021 ◽  
Vol 17 (3) ◽  
pp. 447-461
Author(s):  
Christopher Andreas ◽  
Feevrinna Yohannes Harianto ◽  
Elfhira Juli Safitri ◽  
Nur Chamidah
Keyword(s):  
Nonparametric Regression ◽  
Composite Index ◽  
Negative Relationship ◽  
Least Square ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Absolute Deviation ◽  
Parametric Regression ◽  
Regression Approach ◽  
Repo Rate

During the Covid-19 pandemic, the Indonesia stock market was under great pressure, so that the value of the Jakarta Composite Index (JCI) fluctuated greatly. To maintain economic stability, Bank Indonesia has regulated monetary policy such as setting the BI 7-Days Repo Rate. Analysis of this effect is important to formulate the right policy. This study aims to design the best model in describing the relationship between JCI value and BI 7-Days Repo Rate. The analysis was carried out by using parametric regression approach based on the ordinary least square method and nonparametric regression approach based on least square spline estimator. The results showed that the parametric regression models failed to meet the classical assumptions. Meanwhile, nonparametric regression can produce an optimal model with high accurate prediction, with an overall mean absolute percentage error value of 3.16%. Furthermore, mean square error, coefficient of determination, and mean absolute deviation also show good results. Thus, the effect of the BI 7-Days Repo Rate on the JCI value forms a quadratic pattern, in which a positive relationship is formed when the BI 7-Days Repo Rate is set at more than 4.25% and vice versa for a negative relationship.

scholarly journals Optimization of a Moving Averages Program Using a Simulated Annealing Algorithm: The Goal is to Monitor the Process Not the Patients

2016 ◽  
Vol 62 (10) ◽  
pp. 1361-1371 ◽  
Author(s):  
David Ng ◽  
Frank A Polito ◽  
Mark A Cervinski
Keyword(s):  
Simulated Annealing ◽  
Error Detection ◽  
Simulated Annealing Algorithm ◽  
Moving Average ◽  
Assay Performance ◽  
Published Report ◽  
Protocol Performance ◽  
The Mean ◽  
Annealing Algorithm ◽  
Live Environment

Abstract BACKGROUND The patient moving average (MA) is a QC strategy using the mean patient result to continuously monitor assay performance. Developing sensitive MA protocols that rapidly detect systematic error (SE) is challenging. We compare MA protocols established using a previously published report as a guide and demonstrate the use of a simulated annealing (SA) algorithm to optimize MA protocol performance. METHODS Using 400 days of patient data, we developed MA protocols for 23 assays. MA protocols developed using a previously published report and our SA algorithm were compared using the average number of patient samples affected until error detection (ANPed). RESULTS Comparison of the strategies demonstrated that protocols developed using the SA algorithm generally proved superior. Some analytes such as total protein showed considerable improvement, with positive SE equal to 0.8 g/dL detected with an ANPed of 135 samples using the previously published method whereas the SA algorithm detected this SE with an ANPed of 18. Not all analytes demonstrated similar improvement with the SA algorithm. Phosphorus, for instance, demonstrated only minor improvements, with a positive SE of 0.9 mg/dL detected with an ANPed of 34 using the previously published method vs an ANPed of 29 using the SA algorithm. We also demonstrate an example of SE detection in a live environment using the SA algorithm derived MA protocols. CONCLUSIONS The SA algorithm–developed MA protocols are currently in use in our laboratory and they rapidly detect SE, reducing the number of samples requiring correction and improving patient safety.

scholarly journals The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation

2021 ◽  
Vol 7 ◽  
pp. e623
Author(s):  
Davide Chicco ◽  
Matthijs J. Warrens ◽  
Giuseppe Jurman
Keyword(s):  
Machine Learning ◽  
Regression Analysis ◽  
Binary Classification ◽  
Ground Truth ◽  
Absolute Error ◽  
Supervised Machine Learning ◽  
Coefficient Of Determination ◽  
Target Range ◽  
Percentage Error ◽  
The Mean

Regression analysis makes up a large part of supervised machine learning, and consists of the prediction of a continuous independent target from a set of other predictor variables. The difference between binary classification and regression is in the target range: in binary classification, the target can have only two values (usually encoded as 0 and 1), while in regression the target can have multiple values. Even if regression analysis has been employed in a huge number of machine learning studies, no consensus has been reached on a single, unified, standard metric to assess the results of the regression itself. Many studies employ the mean square error (MSE) and its rooted variant (RMSE), or the mean absolute error (MAE) and its percentage variant (MAPE). Although useful, these rates share a common drawback: since their values can range between zero and +infinity, a single value of them does not say much about the performance of the regression with respect to the distribution of the ground truth elements. In this study, we focus on two rates that actually generate a high score only if the majority of the elements of a ground truth group has been correctly predicted: the coefficient of determination (also known as R-squared or R2) and the symmetric mean absolute percentage error (SMAPE). After showing their mathematical properties, we report a comparison between R2 and SMAPE in several use cases and in two real medical scenarios. Our results demonstrate that the coefficient of determination (R-squared) is more informative and truthful than SMAPE, and does not have the interpretability limitations of MSE, RMSE, MAE and MAPE. We therefore suggest the usage of R-squared as standard metric to evaluate regression analyses in any scientific domain.

scholarly journals The Particle Swarm Optimization Algorithm with Adaptive Chaos Perturbation

2016 ◽  
Vol 11 (6) ◽  
pp. 804 ◽  
Author(s):  
Li Mengxia ◽  
Liao Ruiquan ◽  
Dong Yong
Keyword(s):  
Particle Swarm Optimization ◽  
Simulated Annealing Algorithm ◽  
Heuristic Algorithms ◽  
Particle Swarm ◽  
Population Diversity ◽  
Swarm Optimization ◽  
Chaotic Mapping ◽  
Velocity Information ◽  
The Mean ◽  
Annealing Algorithm

Aiming at the two characteristics of premature convergence of particle swarm optimization that the particle velocity approaches 0 and particle swarm congregate, this paper learns from the annealing function of the simulated annealing algorithm and adaptively and dynamically adjusts inertia weights according to the velocity information of particles to avoid approaching 0 untimely. This paper uses the good uniformity of Anderson chaotic mapping and performs chaos perturbation to part of particles based on the information of variance of the population’s fitness to avoid the untimely aggregation of particle swarm. The numerical simulations of five test functions are performed and the results are compared with several swarm intelligence heuristic algorithms. The results shows that the modified algorithm can keep the population diversity well in the middle stage of the iterative process and it can improve the mean best of the algorithm and the success rate of search.

scholarly journals GOLD PRICESFORECASTING USING TRIPLE EXPONENTIAL METHOD

2021 ◽  
Vol 1 (2) ◽  
pp. 269-276
Author(s):  
Khairawati Khairawati ◽  
Wahyu Fuadi ◽  
Rizki Ramadhansyah ◽  
Dedi Fariadi
Keyword(s):  
Exponential Smoothing ◽  
Gold Price ◽  
Percentage Error ◽  
Price Forecasting ◽  
A Value ◽  
Gold Market ◽  
Exponential Method ◽  
The Mean ◽  
Parameter Values ◽  
Nonlinear Nature

Governments, organizations, and citizens have taken an interest in gold price fluctuations. Gold price forecasting that is accurate may effectively capture price shift tendencies and reduce the effects of gold market volatility. However, due to the multi-factor and nonlinear nature of the gold market. The triple exponential smoothing strategy is used in this study to predict the rise in a value over time since it can replicate trends and seasonal patterns. according to the gold price swings pattern and seasonal components at the same time To calculate system accuracy, the Mean Absolute Percentage Error is employed (MAPE). With alpha 0.15 and beta 0.85 as parameter values, the triple exponential smoothing (TES) approach achieves an accuracy rate of 86.93 percent and a MAPE of 12.49 percent in this study.

scholarly journals Incorporating simulated annealing algorithm in the Weibull distribution for valuation of investment return of Malaysian property development sector

2021 ◽  
Vol 12 ◽  
pp. 22
Author(s):  
Hamza Abubakar ◽  
Shamsul Rijal Muhammad Sabri
Keyword(s):  
Simulated Annealing ◽  
Weibull Distribution ◽  
Differential Evolution ◽  
Simulated Annealing Algorithm ◽  
Estimation Methods ◽  
Coefficient Of Determination ◽  
Property Development ◽  
Investment Return ◽  
Annealing Algorithm

In this study, a simulated annealing algorithm(SAA) has been incorporated in the Weibull Distribution (WD) for Valuation of Investment Return. The purpose is to examine the behaviour of investment's attractiveness in the Malaysian property development sector (MPDS) for a long-term investment period. The research intends is to produce parameters estimates of the WD using MIRR data extracted from the financial report of MPDS for 5 years investment period. The shape parameter of the WD reflects the effectiveness in maximizing the investment performance on MPDS with lower returns and is represented as the slope of the fitted line on a Weibull probability plot. The estimated results obtained using the Simulated annealing algorithm (SAA) has been compared with Differential Evolution (DE) and other existing estimation methods in terms of root mean square error (R-MSE) and coefficient of determination (R-Square). The findings revealed that Weibull distribution parameters estimated via Simulated annealing algorithm have good agreement with parameters estimated via Differential Evolution (DE) and other existing methods based on the transformed MIRR data from the MPDS. The study is expected to provide an overview of the investment behaviour for the long-term investment return in the MPDS. Therefore, SAA in estimating the WD parameters can serve as a good alternative approach for the assessment of the investment potential using MIRR data. The study will be extended to accommodate the growth rate arising from the financial data such as investment growth and insurance claim data.

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