Evolutionary support vector machine for RMB exchange rate forecasting

2019 ◽  
Vol 521 ◽  
pp. 692-704 ◽  
Author(s):  
Sibao Fu ◽  
Yongwu Li ◽  
Shaolong Sun ◽  
Hongtao Li
Keyword(s):  
Support Vector Machine ◽  
Exchange Rate ◽  
Support Vector ◽  
Exchange Rate Forecasting ◽  
Rmb Exchange Rate

scholarly journals Support Vector Regression (SVR) Dalam Memprediksi Harga Minyak Kelapa Sawit di Indonesia dan Nilai Tukar Mata Uang EUR/USD

2021 ◽  
Vol 5 (1) ◽  
pp. 85-92
Author(s):  
Siti Saadah ◽  
Fakhira Zahra Z ◽  
Hasna Haifa Z
Keyword(s):  
Support Vector Machine ◽  
Exchange Rate ◽  
Support Vector Regression ◽  
Support Vector ◽  
Radial Basis

Support Vector Machine merupakan algoritma pembelajaran mesin yang banyak digunakan untuk melakukan prediksi. Salah satunya dengan menggunakan vector kernel radial basis. Dengan karakteristik regresi pada kernel RBF maka metode ini berhasil melakukan prediksi untuk permasalahan seasoning. Mengacu kepada hal tersebut, maka pada penelitian ini akan digunakan pendekatan RBF untuk prediksi forex exchange rate atau minyak kelapa sawit. Karakteristik dua data ini jauh memiliki kesamaan, yakni cenderung ke arah trend seasonal. Mengingat pentingnya dilakukan prediksi untuk kedua studi kasus tersebut, maka kedua permasalahan ini dikaji pada penelitian ini untuk diuji menggunakan algoritma SVR. Hasil yang diperoleh menunjukkan bahwa presentase akurasi untuk exchange rate yaitu 99.97%. Sementara, akurasi pada saat memprediksi minyak kelapa sawit yaitu pada kisaran 98%.

RMB Exchange Rate Forecasting Model Based on Exponential Smoothing and Gray Correlation

2013 ◽  
Vol 401-403 ◽  
pp. 1480-1483
Author(s):  
Bing Xiang Liu ◽  
Yan Wu ◽  
Xiang Cheng
Keyword(s):  
Exchange Rate ◽  
Linear Trend ◽  
Exponential Smoothing ◽  
Exchange Rate Forecasting ◽  
Dynamic Changes ◽  
Rmb Exchange Rate ◽  
Gray Correlation ◽  
Correlation Factors ◽  
The U.S ◽  
Better Than

This paper through the establishment of a Holts linear trend exponential smoothing model, make use of SPSS Clementine for 2005-2010 analysis and forecast of RMB against the U.S. dollar exchange rate, the predicted curve is better than the expectations of the prediction accuracy. To further analyze the dynamic changes of the RMB against the U.S. dollar, method of gray correlation factors that affect the exchange rate is used

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.

scholarly journals Forecasting RMB Exchange Rate Based on a Nonlinear Combination Model of ARFIMA, SVM, and BPNN

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Chi Xie ◽  
Zhou Mao ◽  
Gang-Jin Wang
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Financial Time Series ◽  
Moving Average ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Support Vector ◽  
Rmb Exchange Rate ◽  
Combination Model ◽  
Arfima Model

There are various models to predict financial time series like the RMB exchange rate. In this paper, considering the complex characteristics of RMB exchange rate, we build a nonlinear combination model of the autoregressive fractionally integrated moving average (ARFIMA) model, the support vector machine (SVM) model, and the back-propagation neural network (BPNN) model to forecast the RMB exchange rate. The basic idea of the nonlinear combination model (NCM) is to make the prediction more effective by combining different models’ advantages, and the weight of the combination model is determined by a nonlinear weighted mechanism. The RMB exchange rate against US dollar (RMB/USD) and the RMB exchange rate against Euro (RMB/EUR) are used as the empirical examples to evaluate the performance of NCM. The results show that the prediction performance of the nonlinear combination model is better than the single models and the linear combination models, and the nonlinear combination model is suitable for the prediction of the special time series, such as the RMB exchange rate.

scholarly journals Using Textual and Economic Features to Predict the RMB Exchange Rate

2021 ◽  
pp. 139-158
Author(s):  
Yi-Chen Chung ◽  
Hsien-Ming Chou ◽  
Chih-Neng Hung ◽  
Chihli Hung
Keyword(s):  
Feature Selection ◽  
Exchange Rate ◽  
Ensemble Learning ◽  
Prediction Models ◽  
Gaussian Process Regression ◽  
Support Vector ◽  
Rmb Exchange Rate ◽  
The Exchange Rate ◽  
Basic Prediction ◽  
Feature Selection Techniques

Abstract This research proposes an integrated framework for the use of textual and economic features to predict the exchange rate of the TWD (Taiwan dollar) against the RMB (Chinese Renminbi). The exchange rate is affected by the current economic situation and expectations for the future economic climate. Exchange rate forecasting studies focus mainly on overall economic indices and the actual exchange rate, but overlook the influence of news. This research considers both textual and economic factors and builds three basic prediction models, i.e. multiple linear regression (MLR), support vector regression (SVR), and Gaussian process regression (GPR) for the prediction of the RMB exchange rate. In addition to the three basic prediction models, this research uses ensemble learning and feature selection techniques to improve prediction performance. Our experiments demonstrate that textual features also play an important role in predicting the RMB exchange rate. The SVR model is shown to outperform the other models and the MLR model is shown to perform worst. The ensemble of three basic models performs better than its individual counterparts. Finally, the models which use feature selection techniques demonstrate improved results in general, and different feature selection techniques are shown to be more suitable for different prediction models. JEL classification numbers: D80, F31, F47. Keywords: Exchange rate prediction, Text mining, Ensemble learning, Time series forecasting.

Sign in / Sign up

Export Citation Format

Share Document