[PDF] Top 20 The Corporate Financial Forecasting Based on Least Squares Support Vector Machines Methods
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The Corporate Financial Forecasting Based on Least Squares Support Vector Machines Methods
... into financial forecasting; it can overcome the non-normal, non-linear financial forecasting ...Many financial forecast models were build in the late 90s 20th century, the most ... See full document
7
Time Series Forecasting Using Wavelet Least Squares Support Vector Machines and Wavelet Regression Models for Monthly Stream Flow Data
... Two hybrid wavelet-LSSVM (WLSSVM) model and wavelet-LR (WR) model are obtained by combining two methods, DWT with LSSVM and DWT with LR. Before LSSVM and LR applications, the original time series data were ... See full document
12
Forecast of fund volatility using least squares wavelet support vector regression machines
... The forecasting of financial volatility is important for asset management and portfolio ...forecast financial volatility accurately due to the high nonlinearity and clustering in financial ... See full document
6
Recurrent Support and Relevance Vector Machines Based Model with Application to Forecasting Volatility of Financial Returns
... Of increasing importance in the time series modeling and forecasting is the problem of outliers. Volatility of e- merging stock market returns poses especial challenges in this regard. In sharp contrast to the ... See full document
12
Spatio-temporal avalanche forecasting with Support Vector Machines
... traditional methods such as NN, especially where the data are highly dimensional as is the case in a spatialised ...SVM based forecasts, ranging from simple maps of danger, through aspect/elevation diagrams ... See full document
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Comparative analysis of river flow modelling by using supervised learning technique
... Intelligent Machines technology and high speed data analysis algorithms washed out conventional methods of ...was based on Artificial Neural Networks ...the Support Vector Machine ... See full document
10
Preventing Over-Fitting during Model Selection via Bayesian Regularisation of the Hyper-Parameters
... cross-validation based selection ...of least-squares support vector machines, using model selection procedures with, and without, Bayesian regularisation, (LS-SVM and LS-SVM-BR ... See full document
21
Performance evaluation for engineering project management of particle swarm optimization based on least squares support vector machines
... evaluation methods mainly include Key Performance Index (KPI), Analytic Hierarchy Process (AHP), Extension Theory, Fuzzy Comprehensive Evaluation and so ... See full document
5
Application of LSSVM to logistics demand forecasting based on grey relational analysis and kernel principal component analysis
... Support vector machines (SVM) was first introduced by Vapnik to solve the classification problem ...[3]. Based on the principle of structural risk minimization, SVM has been successfully ... See full document
6
River flow time series using least squares support vector machines
... hybrid based on the predictions of several models fre- quently results in higher prediction accuracy than the pre- diction of an individual ...hybrid forecasting models in hydro- logical processes in order ... See full document
18
Support vector machines applied to the genetic classification problem of hybrid populations with high degrees of similarity
... The ANN analyses gave unsatisfactory results in terms of a solution to the classification problem in populations with high degrees of similarity. The APER values were higher than 70%, being different from those obtained ... See full document
10
Particle swarm optimized partial least square support vector regression model for tax revenue prediction
... From table one, we can see that the growth of tax revenue was relatively stable from 1981 to 1984 but the tax revenue varied greatly from 1984 to 1985while the input variables changed little. This suggests that policy ... See full document
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Title: STOCK MARKET PREDICTION USING MACHINE LEARNING TECHNIQUE
... that support a high generalization capacity and fast ...a least squares cost function to obtain a linear set of equations in a dual space to minimize the computational ...iterative methods, ... See full document
5
New Intelligent Classification Techniques for Diagnosis of Diabetes Mellitus based on Modified PSO
... In this section, we present the performance evaluation methods used to evaluate the proposed method. Finally, we give the experimental results and discuss our observations from the obtained results. The proposed ... See full document
7
Applying Principal Component Analysis, Genetic Algorithm and Support Vector Machine for Risk Forecasting of General Contracting
... According to characteristics and risks of the general engineering, the paper brings forward the risk that the company should be pay attention to, allowing for the theory of total risk management and system and the ... See full document
7
Support Vector Machine and Least Square Support Vector Machine Stock Forecasting Models
... the least difference ...of financial tsunami is ...and least difference7.6761 for 4 days and best average 2.7868 and least difference ...and least difference 21.174 for 4 days and best ... See full document
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Combine holts winter and support vector machines in forecasting time series
... series forecasting is an important practical problem with a diverse range of applications in many observational disciplines, such as finance, economics, meteorology, biology, medicine, hydrology, oceanography, ... See full document
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← Return to Article Details ENHANCEMENT SPECTRAL RESOLUTION FOR THE PREDICTION AMOUNT OF SOFOSBUVIR AND LEDIPASVIR USING LEAST SQUARES SUPPORT VECTOR MACHINE AND ARTIFICIAL NEURAL NETWORKS IN PHARMACEUTICAL FORMULATION
... Where y pred is predicted value in the sample, y obs is the observed value of the sample and n is the number of samples in the validation set 30 . The results of the prediction of concentrations related to synthetic ... See full document
14
Combine holts winter and support vector machines in forecasting time serie
... Risk Minimization principle from the statistical learning theory and this gained popularity due to its many attraction features and promising empirical performance. Han et al (2007) also viewed that SVM has been proved ... See full document
25
On the ease of predicting the thermodynamic properties of beta-cyclodextrin inclusion complexes
... regression methods, namely principal components regression (PCR) [14], partial least squares regression (PLSR) [14] and support vector regression with forward feature selection ... See full document
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