A multiple support vector machine approach to stock index forecasting with mixed frequency sampling

2017 ◽  
Vol 122 ◽  
pp. 90-102 ◽  
Author(s):  
Yuchen Pan ◽  
Zhi Xiao ◽  
Xianning Wang ◽  
Daoli Yang
Keyword(s):  
Support Vector Machine ◽  
Stock Index ◽  
Support Vector ◽  
Mixed Frequency ◽  
Frequency Sampling ◽  
Multiple Support ◽  
Stock Index Forecasting

scholarly journals ℓp-Norm Multikernel Learning Approach for Stock Market Price Forecasting

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Xigao Shao ◽  
Kun Wu ◽  
Bifeng Liao
Keyword(s):  
Regression Model ◽  
Support Vector Regression ◽  
Market Price ◽  
Stock Index ◽  
Support Vector ◽  
Optimization Strategy ◽  
Support Vector Regression Model ◽  
Stock Price Prediction ◽  
Multiple Kernel ◽  
Multiple Support

Linear multiple kernel learning model has been used for predicting financial time series. However,ℓ1-norm multiple support vector regression is rarely observed to outperform trivial baselines in practical applications. To allow for robust kernel mixtures that generalize well, we adoptℓp-norm multiple kernel support vector regression (1≤p<∞) as a stock price prediction model. The optimization problem is decomposed into smaller subproblems, and the interleaved optimization strategy is employed to solve the regression model. The model is evaluated on forecasting the daily stock closing prices of Shanghai Stock Index in China. Experimental results show that our proposed model performs better thanℓ1-norm multiple support vector regression model.

scholarly journals Cerebral infarction classification using multiple support vector machine with information gain feature selection

2020 ◽  
Vol 9 (4) ◽  
pp. 1578-1584
Author(s):  
Zuherman Rustam ◽  
Arfiani Arfiani ◽  
Jacub Pandelaki
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Cerebral Infarction ◽  
Information Gain ◽  
The Elderly ◽  
Common Type ◽  
Support Vector ◽  
Classification Result ◽  
The Third ◽  
Multiple Support

Stroke ranks the third leading cause of death in the world after heart disease and cancer. It also occupies the first position as a disease that causes both mild and severe disability. The most common type of stroke is cerebral infarction, which increases every year in Indonesia. This disease does not only occur in the elderly, but in young and productive people which makes early detection very important. Although there are varied of medical methods used to classify cerebral infarction, this study uses a multiple support vector machine with information gain feature selection (MSVM-IG). MSVM-IG is a modification among IG Feature Selection and SVM, where SVM conducted doubly in the process of classification which utilizes the support vector as a new dataset. The data obtained from Cipto Mangunkusumo Hospital, Jakarta. Based on the results, the proposed method was able to achieve an accuracy value of 81%, therefore, this method can be considered to use for better classification result.

scholarly journals KOMPARASI MODEL SUPPORT VECTOR MACHINES (SVM) DAN NEURAL NETWORK UNTUK MENGETAHUI TINGKAT AKURASI PREDIKSI TERTINGGI HARGA SAHAM

2017 ◽  
Vol 3 (1) ◽  
Author(s):  
R. Hadapiningradja Kusumodestoni ◽  
Sarwido Sarwido
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Stock Prices ◽  
Prediction Accuracy ◽  
High Accuracy ◽  
Stock Index ◽  
Support Vector ◽  
Accuracy Rate ◽  
Trend Prediction ◽  
Data Set

There are many types of investments to make money, one of which is in the form of shares. Shares is a trading company dealing with securities in the global capital markets. Stock Exchange or also called stock market is actually the activities of private companies in the form of buying and selling investments. To avoid losses in investing, we need a model of predictive analysis with high accuracy and supported by data - lots of data and accurately. The correct techniques in the analysis will be able to reduce the risk for investors in investing. There are many models used in the analysis of stock price movement prediction, in this study the researchers used models of neural networks (NN) and a model of support vector machine (SVM). Based on the background of the problems that have been mentioned in the previous description it can be formulated the problem as follows: need an algorithm that can predict stock prices, and need a high accuracy rate by adding a data set on the prediction, two algorithms will be investigated expected results last researchers can deduce where the algorithm accuracy rate predictions are the highest or accurate, then the purpose of this study was to mengkomparasi or compare between the two algorithms are algorithms Neural Network algorithm and Support Vector Machine which later on the end result has an accuracy rate forecast stock prices highest to see the error value RMSEnya. After doing research using the model of neural network and model of support vector machine (SVM) to predict the stock using the data value of the shares on the stock index hongkong dated July 20, 2016 at 16:26 pm until the date of 15 September 2016 at 17:40 pm as many as 729 data sets within an interval of 5 minute through a process of training, learning, and then continue the process of testing so the result is that by using a neural network model of the prediction accuracy of 0.503 +/- 0.009 (micro 503) while using the model of support vector machine (SVM) accuracy of the predictions for 0477 + / - 0.008 (micro: 0477) so that after a comparison can be concluded that the neural network models have trend prediction accuracy higher than the model of support vector machine (SVM).

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