scholarly journals PENGEMBANGAN MODEL SUPPORT VECTOR MACHINES (SVM) DENGAN MEMPERBANYAK DATASET UNTUK PREDIKSI BISNIS FOREX MENGGUNAKAN METODE KERNEL TRICK

2017 ◽  
Vol 3 (1) ◽  
Author(s):  
Adi Sucipto ◽  
Akhmad Khanif Zyen
Keyword(s):  
Support Vector Machine ◽  
Exchange Rate ◽  
Support Vector Machine Model ◽  
Precious Metals ◽  
Current Data ◽  
Support Vector ◽  
Machine Model ◽  
Kernel Trick ◽  
Currency Exchange ◽  
Currency Exchange Rate

There are many types of investments that can be used to generate income, such as in the form of land, houses, gold, precious metals etc., there are also in the form of financial assets such as stocks, mutual funds, bonds and money markets or capital markets. One of the investments that attract enough attention today is the capital market investment. The purpose of this study is to predict and improve the accuracy of foreign exchange rates on forex business by using the Support Vector Machine model as a model for predicting and using more data sets compared with previous research that is as many as 1558 dataset. This study uses currency exchange rate data obtained from PT. Best Profit Future Cab. Surabaya is already in the form of data consisting of open, high, low, close attributes by using the current data of Euro currency exchange rate to USA Dollar with period every 1 minutes from May 12, 2016 at 09.51 until 13 May 2016 at 12:30 As much as 1689 dataset, After conducting research using Support Vector Machine model with kernel trick method to predict Forex using current data of Euro exchange rate to USA Dollar with period every 1 minutes from May 12, 2016 at 09.51 until 13 May 2016 at 12:30 as much as 1689 The dataset yielded a considerable prediction accuracy of 97.86%, with this considerable accuracy indicating that the movement of the Euro currency exchange rate to the USA Dollar on May 12 to May 13, 2016 can be predicted precisely.

Currency Crisis Prediction Using Wavelet-Based Support Vector Machine

2014 ◽  
Vol 989-994 ◽  
pp. 2560-2564
Author(s):  
Qin Li Zhang
Keyword(s):  
Support Vector Machine ◽  
Exchange Rate ◽  
Wavelet Decomposition ◽  
Currency Crisis ◽  
Support Vector ◽  
Learning Paradigm ◽  
Currency Exchange ◽  
Currency Exchange Rate ◽  
Crisis Events ◽  
Decomposition Procedure

A currency crisis is typically a kind of rare event, but it hurts sustainable economic development when it occurs. A novel method of wavelet-based support vector machine (SVM) is proposed to predict financial crisis events for early-warning purposes in this paper. In the proposed method, currency exchange rate, a typical currency indicator that usually reflects economic fluctuations, is first chosen. Then a wavelet decomposition algorithm is applied to the currency exchange rate series. Using the wavelet decomposition procedure, some details and features of the currency exchange rate series, with different scales, can be obtained. Using these details and features, a wavelet-based SVM learning paradigm is used to predict future currency crisis events, based upon some historical data. For illustration purpose, the proposed wavelet-based SVM learning paradigm is applied to exchange rate data of two Asian countries to evaluate the state of currency crisis. Experimental results reveal that the proposed wavelet-based SVM learning paradigm can significantly improve the generalization performance relative to some popular forecasting methods.

scholarly journals An Improved Whale Algorithm for Support Vector Machine Prediction of Photovoltaic Power Generation

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 212
Author(s):  
Yu-Wei Liu ◽  
Huan Feng ◽  
Heng-Yi Li ◽  
Ling-Ling Li
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Output Power ◽  
Input Data ◽  
Support Vector Machine Model ◽  
Weather Conditions ◽  
Clean Energy ◽  
Support Vector ◽  
Machine Model ◽  
Photovoltaic Power

Accurate prediction of photovoltaic power is conducive to the application of clean energy and sustainable development. An improved whale algorithm is proposed to optimize the Support Vector Machine model. The characteristic of the model is that it needs less training data to symmetrically adapt to the prediction conditions of different weather, and has high prediction accuracy in different weather conditions. This study aims to (1) select light intensity, ambient temperature and relative humidity, which are strictly related to photovoltaic output power as the input data; (2) apply wavelet soft threshold denoising to preprocess input data to reduce the noise contained in input data to symmetrically enhance the adaptability of the prediction model in different weather conditions; (3) improve the whale algorithm by using tent chaotic mapping, nonlinear disturbance and differential evolution algorithm; (4) apply the improved whale algorithm to optimize the Support Vector Machine model in order to improve the prediction accuracy of the prediction model. The experiment proves that the short-term prediction model of photovoltaic power based on symmetry concept achieves ideal accuracy in different weather. The systematic method for output power prediction of renewable energy is conductive to reducing the workload of predicting the output power and to promoting the application of clean energy and sustainable development.

Research on SVM Line Loss Rate Prediction Based on Heuristic Algorithm

2013 ◽  
Vol 291-294 ◽  
pp. 2164-2168 ◽  
Author(s):  
Li Tian ◽  
Qiang Qiang Wang ◽  
An Zhao Cao
Keyword(s):  
Support Vector Machine ◽  
Heuristic Algorithm ◽  
Support Vector Machine Model ◽  
Loss Rate ◽  
Heuristic Algorithms ◽  
Support Vector ◽  
Machine Model ◽  
Rate Prediction ◽  
Line Loss

With the characteristic of line loss volatility, a research of line loss rate prediction was imperatively carried out. Considering the optimization ability of heuristic algorithm and the regression ability of support vector machine, a heuristic algorithm-support vector machine model is constructed. Case study shows that, compared with other heuristic algorithms’, the search efficiency and speed of genetic algorithm are good, and the prediction model is with high accuracy.

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