scholarly journals Analisis Pola Prediksi Data Time Series menggunakan Support Vector Regression, Multilayer Perceptron, dan Regresi Linear Sederhana

2019 ◽  
Vol 3 (2) ◽  
pp. 282-287
Author(s):  
Ika Oktavianti ◽  
Ermatita Ermatita ◽  
Dian Palupi Rini
Keyword(s):  
Time Series ◽  
Support Vector Regression ◽  
Multilayer Perceptron ◽  
Time Series Data ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Series Data ◽  
Support Vector ◽  
Contributing Factors ◽  
Testing Data

Licensing services is one of the forms of public services that important in supporting increased investment in Indonesia and is currently carried out by the Investment and Licensing Services Department. The problems that occur in general are the length of time to process licenses and one of the contributing factors is the limited number of licensing officers. Licensing data is a time series data which have monthly observation. The Artificial Neural Network (ANN) and Support Vector Machine (SVR) is used as machine learning techniques to predict licensing pattern based on time series data. Of the data used dataset 1 and dataset 2, the sharing of training data and testing data is equal to 70% and 30% with consideration that training data must be more than testing data. The result of the study showed for Dataset 1, the ANN-Multilayer Perceptron have a better performance than Support Vector Regression (SVR) with MSE, MAE and RMSE values is 251.09, 11.45, and 15.84. Then for dataset 2, SVR-Linear has better performance than MLP with values of MSE, MAE and RMSE of 1839.93, 32.80, and 42.89. The dataset used to predict the number of permissions is dataset 2. The study also used the Simple Linear Regression (SLR) method to see the causal relationship between the number of licenses issued and licensing service officers. The result is that the relationship between the number of licenses issued and the number of service officers is less significant because there are other factors that affect the number of licenses.  

scholarly journals Peramalan Harga Emas Saat Pandemi Covid-19 Menggunakan Model Hybrid Autoregressive Integrated Moving Average - Support Vector Regression

2021 ◽  
Vol 1 (1) ◽  
pp. 52-65
Author(s):  
Drajat Indra Purnama
Keyword(s):  
Support Vector Regression ◽  
Hybrid Model ◽  
Moving Average ◽  
Arima Model ◽  
Training Data ◽  
Series Data ◽  
Support Vector ◽  
Autoregressive Integrated Moving Average ◽  
Testing Data ◽  
Linearity Test

ABSTRAKInvestasi emas merupakan salah satu investasi yang menjadi favorit dimasa pandemi Covid 19 seperti sekarang ini. Hal ini dikarenakan harga emas yang nilainya relatif fluktuatif tetapi menunjukkan tren peningkatan. Investor dituntut pandai dalam berinvestasi emas, mampu memprediksi peluang dimasa yang akan datang. Salah satu model peramalan data deret waktu adalah model Autoregressive Integrated Moving Average (ARIMA). Model ARIMA baik digunakan pada data yang berpola linear tetapi jika digunakan pada data data nonlinear keakuratannya menurun. Untuk mengatasi permasalahan data nonlinear dapat menggunakan model Support Vector Regression (SVR). Pengujian linearitas pada data harga emas menunjukkan adanya pola data linear dan nonlinear sekaligus sehingga digunakan kombinasi ARIMA dan SVR yaitu model hybrid ARIMA-SVR. Hasil peramalan menggunakan model hybrid ARIMA-SVR menunjukkan hasil lebih baik dibanding model ARIMA. Hal ini dibuktikan dengan nilai MAPE model hybrid ARIMA-SVR lebih kecil dibandingkan nilai MAPE model ARIMA. Nilai MAPE model hybrid ARIMA-SVR sebesar 0,355 pada data training dan 4,001 pada data testing, sedangkan nilai MAPE model ARIMA sebesar 0,903 pada data training dan 4,076 pada data testing.ABSTRACTGold investment is one of the favorite investments during the Covid 19 pandemic as it is today. This is because the price of gold is relatively volatile but shows an increasing trend. Investors are required to be smart in investing in gold, able to predict future opportunities. One of the time series data forecasting models is the Autoregressive Integrated Moving Average (ARIMA) model. The ARIMA model is good for use on linear patterned data but if it is used on nonlinear data the accuracy decreases. To solve the problem of nonlinear data, you can use the Support Vector Regression (SVR) model. The linearity test on the gold price data shows that there are linear and nonlinear data patterns at the same time so that a combination of ARIMA and SVR is used, namely the ARIMA-SVR hybrid model. Forecasting results using the ARIMA-SVR hybrid model show better results than the ARIMA model. This is evidenced by the MAPE value of the ARIMA-SVR hybrid model which is smaller than the MAPE value of the ARIMA model. The MAPE value of the ARIMA-SVR hybrid model is 0.355 on the training data and 4.001 on the testing data, while the MAPE value of the ARIMA model is 0.903 in the training data and 4.076 in the testing data.

scholarly journals Peramalan Harga Emas Saat Pandemi Covid-19 Menggunakan Model Hybrid Autoregressive Integrated Moving Average - Support Vector Regression

2021 ◽  
Author(s):  
Drajat Indra Purnama
Keyword(s):  
Support Vector Regression ◽  
Hybrid Model ◽  
Moving Average ◽  
Arima Model ◽  
Training Data ◽  
Series Data ◽  
Support Vector ◽  
Autoregressive Integrated Moving Average ◽  
Testing Data ◽  
Linearity Test

Gold investment is one of the favorite investments during the Covid 19 pandemic as it is today. This is because the price of gold is relatively volatile but shows an increasing trend. Investors are required to be smart in investing in gold, able to predict future opportunities. One of the time series data forecasting models is the Autoregressive Integrated Moving Average (ARIMA) model. The ARIMA model is good for use on linear patterned data but if it is used on nonlinear data the accuracy decreases. To solve the problem of nonlinear data, you can use the Support Vector Regression (SVR) model. The linearity test on the gold price data shows that there are linear and nonlinear data patterns at the same time so that a combination of ARIMA and SVR is used, namely the ARIMA-SVR hybrid model. Forecasting results using the ARIMA-SVR hybrid model show better results than the ARIMA model. This is evidenced by the MAPE value of the ARIMA-SVR hybrid model which is smaller than the MAPE value of the ARIMA model. The MAPE value of the ARIMA-SVR hybrid model is 0.355 on the training data and 4.001 on the testing data, while the MAPE value of the ARIMA model is 0.903 in the training data and 4.076 in the testing data.

scholarly journals FPGA-Based Implementation of a Multilayer Perceptron Suitable for Chaotic Time Series Prediction

Technologies ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 90 ◽  
Author(s):  
Ana Pano-Azucena ◽  
Esteban Tlelo-Cuautle ◽  
Sheldon Tan ◽  
Brisbane Ovilla-Martinez ◽  
Luis de la Fraga
Keyword(s):  
Time Series ◽  
Multilayer Perceptron ◽  
Time Series Data ◽  
Mean Squared Error ◽  
Time Series Prediction ◽  
Chaotic Time Series ◽  
Series Data ◽  
Support Vector ◽  
Maximum Lyapunov Exponent ◽  
Inference System

Many biological systems and natural phenomena exhibit chaotic behaviors that are saved in time series data. This article uses time series that are generated by chaotic oscillators with different values of the maximum Lyapunov exponent (MLE) to predict their future behavior. Three prediction techniques are compared, namely: artificial neural networks (ANNs), the adaptive neuro-fuzzy inference system (ANFIS) and least-squares support vector machines (SVM). The experimental results show that ANNs provide the lowest root mean squared error. That way, we introduce a multilayer perceptron that is implemented using a field-programmable gate array (FPGA) to predict experimental chaotic time series.

scholarly journals A Novel Time Series Prediction Approach Based on a Hybridization of Least Squares Support Vector Regression and Swarm Intelligence

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Nhat-Duc Hoang ◽  
Anh-Duc Pham ◽  
Minh-Tu Cao
Keyword(s):  
Time Series ◽  
Least Squares ◽  
Support Vector Regression ◽  
Time Series Data ◽  
Time Series Prediction ◽  
Hybrid Approach ◽  
Series Data ◽  
Support Vector ◽  
Promising Alternative ◽  
Prediction Approach

This research aims at establishing a novel hybrid artificial intelligence (AI) approach, named as firefly-tuned least squares support vector regression for time series prediction(FLSVRTSP). The proposed model utilizes the least squares support vector regression (LS-SVR) as a supervised learning technique to generalize the mapping function between input and output of time series data. In order to optimize the LS-SVR’s tuning parameters, theFLSVRTSPincorporates the firefly algorithm (FA) as the search engine. Consequently, the newly construction model can learn from historical data and carry out prediction autonomously without any prior knowledge in parameter setting. Experimental results and comparison have demonstrated that theFLSVRTSPhas achieved a significant improvement in forecasting accuracy when predicting both artificial and real-world time series data. Hence, the proposed hybrid approach is a promising alternative for assisting decision-makers to better cope with time series prediction.

scholarly journals Peramalan Harga Emas Saat Pandemi Covid-19 Menggunakan Model Hybrid Autoregressive Integrated Moving Average - Support Vector Regression

2021 ◽  
Vol 3 (1) ◽  
pp. 52-65
Author(s):  
Drajat Indra Purnama
Keyword(s):  
Support Vector Regression ◽  
Hybrid Model ◽  
Moving Average ◽  
Arima Model ◽  
Training Data ◽  
Series Data ◽  
Support Vector ◽  
Autoregressive Integrated Moving Average ◽  
Testing Data ◽  
Linearity Test

ABSTRAKInvestasi emas merupakan salah satu investasi yang menjadi favorit dimasa pandemi Covid 19 seperti sekarang ini. Hal ini dikarenakan harga emas yang nilainya relatif fluktuatif tetapi menunjukkan tren peningkatan. Investor dituntut pandai dalam berinvestasi emas, mampu memprediksi peluang dimasa yang akan datang. Salah satu model peramalan data deret waktu adalah model Autoregressive Integrated Moving Average (ARIMA). Model ARIMA baik digunakan pada data yang berpola linear tetapi jika digunakan pada data data nonlinear keakuratannya menurun. Untuk mengatasi permasalahan data nonlinear dapat menggunakan model Support Vector Regression (SVR). Pengujian linearitas pada data harga emas menunjukkan adanya pola data linear dan nonlinear sekaligus sehingga digunakan kombinasi ARIMA dan SVR yaitu model hybrid ARIMA-SVR. Hasil peramalan menggunakan model hybrid ARIMA-SVR menunjukkan hasil lebih baik dibanding model ARIMA. Hal ini dibuktikan dengan nilai MAPE model hybrid ARIMA-SVR lebih kecil dibandingkan nilai MAPE model ARIMA. Nilai MAPE model hybrid ARIMA-SVR sebesar 0,355 pada data training dan 4,001 pada data testing, sedangkan nilai MAPE model ARIMA sebesar 0,903 pada data training dan 4,076 pada data testing.ABSTRACTGold investment is one of the favorite investments during the Covid 19 pandemic as it is today. This is because the price of gold is relatively volatile but shows an increasing trend. Investors are required to be smart in investing in gold, able to predict future opportunities. One of the time series data forecasting models is the Autoregressive Integrated Moving Average (ARIMA) model. The ARIMA model is good for use on linear patterned data but if it is used on nonlinear data the accuracy decreases. To solve the problem of nonlinear data, you can use the Support Vector Regression (SVR) model. The linearity test on the gold price data shows that there are linear and nonlinear data patterns at the same time so that a combination of ARIMA and SVR is used, namely the ARIMA-SVR hybrid model. Forecasting results using the ARIMA-SVR hybrid model show better results than the ARIMA model. This is evidenced by the MAPE value of the ARIMA-SVR hybrid model which is smaller than the MAPE value of the ARIMA model. The MAPE value of the ARIMA-SVR hybrid model is 0.355 on the training data and 4.001 on the testing data, while the MAPE value of the ARIMA model is 0.903 in the training data and 4.076 in the testing data.

scholarly journals Novelty detection for time series data analysis in water distribution systems using support vector machines

2010 ◽  
Vol 13 (4) ◽  
pp. 672-686 ◽  
Author(s):  
Stephen R. Mounce ◽  
Richard B. Mounce ◽  
Joby B. Boxall
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Support Vector Regression ◽  
Distribution Systems ◽  
Water Distribution ◽  
Time Series Data ◽  
Water Distribution Systems ◽  
Kernel Functions ◽  
Series Data ◽  
Support Vector

The sampling frequency and quantity of time series data collected from water distribution systems has been increasing in recent years, giving rise to the potential for improving system knowledge if suitable automated techniques can be applied, in particular, machine learning. Novelty (or anomaly) detection refers to the automatic identification of novel or abnormal patterns embedded in large amounts of “normal” data. When dealing with time series data (transformed into vectors), this means abnormal events embedded amongst many normal time series points. The support vector machine is a data-driven statistical technique that has been developed as a tool for classification and regression. The key features include statistical robustness with respect to non-Gaussian errors and outliers, the selection of the decision boundary in a principled way, and the introduction of nonlinearity in the feature space without explicitly requiring a nonlinear algorithm by means of kernel functions. In this research, support vector regression is used as a learning method for anomaly detection from water flow and pressure time series data. No use is made of past event histories collected through other information sources. The support vector regression methodology, whose robustness derives from the training error function, is applied to a case study.

Performance degradation prediction of aeroengine based on attention model and support vector regression

2021 ◽  
pp. 095441002110147
Author(s):  
Changchang Che ◽  
Huawei Wang ◽  
Xiaomei Ni ◽  
Qiang Fu
Keyword(s):  
Time Series ◽  
Support Vector Regression ◽  
Time Series Data ◽  
Time Series Prediction ◽  
Performance Degradation ◽  
Series Data ◽  
Support Vector ◽  
Attention Model ◽  
Multiple State ◽  
And Performance

Accurate performance degradation prediction of aeroengines can ensure the safety and reliability of the aircraft. Based on the mass long time series data of multiple state parameters, a novel performance degradation prediction method based on attention model (AM) and support vector regression (SVR) is proposed in this article. The AM uses the attention mechanism between encoder and decoder to realize weight distribution of different source samples, so as to realize time series prediction of state parameters. The SVR model is used to mine the mapping relationship between multiple state parameters and performance degradation. The performance degradation prediction results can be achieved by putting the time series prediction results of multiple state parameters into the SVR model. The turbofan engine degradation simulation dataset carried out using commercial modular aero-propulsion system simulation (C-MAPSS) is used to verify the effectiveness of the proposed method. The results demonstrate that it can get accurate time series prediction and performance degradation analysis results. Compared with other methods, the proposed attention model and support vector regression (AM-SVR) model has lower prediction error and higher stability when dealing with noised samples.

scholarly journals Planning Total Veener Production PT. XYZ

2021 ◽  
Vol 1 (2) ◽  
Author(s):  
Krisna Risky Putra Irawan ◽  
Tedjo Sukmono
Keyword(s):  
Support Vector Regression ◽  
Time Series Data ◽  
Training Data ◽  
Series Data ◽  
Support Vector ◽  
Efficient Production ◽  
Production Forecasting ◽  
Data Application ◽  
Rbf Kernel ◽  
Accurate Forecast

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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