Application of Hybrid RBF Neural Network Ensemble Model Based on Wavelet Support Vector Machine Regression in Rainfall Time Series Forecasting

2012 ◽  
Author(s):  
Lingzhi Wang ◽  
Jiansheng Wu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Support Vector Machine ◽  
Rbf Neural Network ◽  
Time Series Forecasting ◽  
Support Vector ◽  
Rainfall Time Series ◽  
Ensemble Model ◽  
Neural Network Ensemble ◽  
Support Vector Machine Regression

scholarly journals Predicting the Direction Movement of Financial Time Series Using Artificial Neural Network and Support Vector Machine

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Muhammad Ali ◽  
Dost Muhammad Khan ◽  
Muhammad Aamir ◽  
Amjad Ali ◽  
Zubair Ahmad
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Stock Exchange ◽  
Financial Time Series ◽  
Composite Index ◽  
Support Vector ◽  
Financial Time ◽  
Svm Model

Prediction of financial time series such as stock and stock indexes has remained the main focus of researchers because of its composite nature and instability in almost all of the developing and advanced countries. The main objective of this research work is to predict the direction movement of the daily stock prices index using the artificial neural network (ANN) and support vector machine (SVM). The datasets utilized in this study are the KSE-100 index of the Pakistan stock exchange, Korea composite stock price index (KOSPI), Nikkei 225 index of the Tokyo stock exchange, and Shenzhen stock exchange (SZSE) composite index for the last ten years that is from 2011 to 2020. To build the architect of a single layer ANN and SVM model with linear, radial basis function (RBF), and polynomial kernels, different technical indicators derived from the daily stock trading, such as closing, opening, daily high, and daily low prices and used as input layers. Since both the ANN and SVM models were used as classifiers; therefore, accuracy and F-score were used as performance metrics calculated from the confusion matrix. It can be concluded from the results that ANN performs better than SVM model in terms of accuracy and F-score to predict the direction movement of the KSE-100 index, KOSPI index, Nikkei 225 index, and SZSE composite index daily closing price movement.

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