Forecasting Inflation Rate Using Support Vector Regression (SVR) Based Weight Attribute Particle Swarm Optimization (WAPSO)

Priliani, Erlin Mega; Putra, Anggyi Trisnawan; Muslim, Much Aziz

doi:10.15294/sji.v5i2.14613

Cited by 10 publications

(12 citation statements)

References 10 publications

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“…Data deret waktu merupakan jenis data yang sering dikembangkan untuk kasus peramalan. Peramalan yang menggunakan data deret waktu dalam perkembangannya menunjukkan bahwa keakuratan peramalan dapat ditingkatkan dengan menggabungkan beberapa model dengan kombinasi daripada hanya menggunakan salah satu model terbaik [5].…”

Section: Data Time Seriesunclassified

“…Maka dari itu metode yang dipilih untuk menormalisasi data adalah metode min-max dikarenakan metode normalisasi ini menskalakan ulang secara Linear data dari satu rentang nilai untuk rentang baru nilai-nilai, seperti [0,1] atau [-1,1]. Proses normalisasi dilakukan sebelum melakukan pelatihan menggunakan SVR dengan menggunakan rumus [5]:…”

Section: Normalisasi Dan Denormalisasi Dataunclassified

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Planning Total Veener Production PT. XYZ

Irawan

Sukmono

2021

PELS

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PT. XYZ is engaged in the manufacture and sale of wood veneers. Starting from the constant occurrence of over stock, now the company must make improvements to the production forecasting process so that over stock can be avoided. It can be seen that accurate production forecasting can create conditions for an effective and efficient production system. This study aims to obtain a more accurate forecast of material requirements using the Support Vector Regression (SVR) method, which is the result of the development of a Support Vector Machine (SVM) which has good performance in predicting time series data. Application of the Support Vector Regression (SVR) method with the RBF kernel in predicting the need for veneer production using the MATLAB application, it produces the smallest error rate with a MAPE of 5%, RMSE of 4364.63 and of 0.748274147. on 67 training data and 20 testing data.

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Section: Data Time Seriesunclassified

Section: Normalisasi Dan Denormalisasi Dataunclassified

Planning Total Veener Production PT. XYZ

Irawan

Sukmono

2021

PELS

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“…The transformation process is carried out using min-max normalization, as shown in (1). This normalization can accelerate the learning process involving data in the same scale value [17].…”

Section: Data Preprocessingmentioning

confidence: 99%

“…SVR is also proven to be better than Backpropagation for forecasting oil palm production [16]. However, similar to SVM, SVR cannot determine the appropriate parameters to produce optimal results, whereas the selection of appropriate parameters will be able to improve the accuracy of the results produced [17].…”

Section: Introductionmentioning

confidence: 99%

Forecasting Model of Staple Food Prices Using Support Vector Regression with Optimized Parameters

Astiningrum

Wijayaningrum

Putri

2021

Jurnal Ilmiah Teknik Elektro Komputer Dan Informatika

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The large number of Indonesians who consume rice as their primary food makes rice price a benchmark for determining the other staple food prices. The instability of rice prices due to climate change or other uncontrollable factors makes it difficult for Indonesians to estimate the rice prices, especially for the poor. This study proposes the usage of the Improved Crow Search Algorithm (ICSA) to optimize the Support Vector Regression (SVR) parameter in building a regression model to predict the price of staple foods. The forecasting process is carried out based on time series data of 11 staples for four years. The proposed ICSA optimizes the six parameters used in the SVR to form a regression model, consisting of lambda, epsilon, sigma, learning rate, soft margin constant, and the number of iterations. Algorithm performance is measured using MAPE and NRMSE by comparing the actual price of staple foods and forecasting results to get the error rate. With this parameter optimization mechanism, the forecasting results given are good enough with a small error value, in the form of MAPE of 17.081 and NRMSE of 1.594. A MAPE value between 10 and 20 indicates that the forecasting result is acceptable, while an NRMSE value of less than 10 indicates that the forecasting accuracy is excellent. The improvised technique on Crow Search Algorithm is proven to improve the performance of Support Vector Regression in forecasting the price of staple foods.

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“…The SVR method can also be added with the use of optimization and one of the optimizations is by using Particle Swarm Optimization (PSO). Where the addition of PSO optimization can improve the accuracy of the forecasting done [10].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Regression, Support Vector Regression (SVR), and SVR-Particle Swarm Optimization (PSO) for Rainfall Forecasting

Yulianto

Mahmudy

Soebroto

2020

JITeCS

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Rainfall is one of the factors that influence climate change in an area and is very difficult to predict, while rainfall information is very important for the community. Forecasting can be done using existing historical data with the help of mathematical computing in modeling. The Support Vector Regression (SVR) method is one method that can be used to predict non-linear rainfall data using a regression function. In calculations using the regression function, choosing the right SVR parameters is needed to produce forecasting with high accuracy. Particle Swarm Optimization (PSO) method is one method that can be used to optimize the parameters of the existing SVR method, so that it will produce SVR parameter values with high accuracy. Forecasting with rainfall data in Poncokusumo region using SVR-PSO has a performance evaluation value that refers to the value of Root Mean Square Error (RMSE). There are several Kernels that will be used in predicting rainfall using Regression, SVR, and SVR-PSO with Linear Kernels, Gaussian RBF Kernels, ANOVA RBF Kernels. The results of the performance evaluation values obtained by referring to the RMSE value for Regression is 56,098, SVR is 88,426, SVR-PSO method with Linear Kernel is 7.998, SVR-PSO method with Gaussian RBF Kernel is 27.172, and SVR-PSO method with ANOVA RBF Kernel is 2.193. Based on research that has been done, ANOVA RBF Kernel is a good Kernel on the SVR-PSO method for use in rainfall forecasting, because it has the best forecasting accuracy with the smallest RMSE value.

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Forecasting Inflation Rate Using Support Vector Regression (SVR) Based Weight Attribute Particle Swarm Optimization (WAPSO)

Cited by 10 publications

References 10 publications

Planning Total Veener Production PT. XYZ

Planning Total Veener Production PT. XYZ

Forecasting Model of Staple Food Prices Using Support Vector Regression with Optimized Parameters

Comparison of Regression, Support Vector Regression (SVR), and SVR-Particle Swarm Optimization (PSO) for Rainfall Forecasting

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