A Review on Optimization of Least Squares Support Vector Machine for Time Series Forecasting
نویسندگان
چکیده
Support Vector Machine has appeared as an active study in machine learning community and extensively used in various fields including in prediction, pattern recognition and many more. However, the Least Squares Support Vector Machine which is a variant of Support Vector Machine offers better solution strategy. In order to utilize the LSSVM capability in data mining task such as prediction, there is a need to optimize its hyper parameters. This paper presents a review on techniques used to optimize the parameters based on two main classes; Evolutionary Computation and Cross Validation.
منابع مشابه
OPTIMAL SHAPE DESIGN OF GRAVITY DAMS BASED ON A HYBRID META-HERURISTIC METHOD AND WEIGHTED LEAST SQUARES SUPPORT VECTOR MACHINE
A hybrid meta-heuristic optimization method is introduced to efficiently find the optimal shape of concrete gravity dams including dam-water-foundation rock interaction subjected to earthquake loading. The hybrid meta-heuristic optimization method is based on a hybrid of gravitational search algorithm (GSA) and particle swarm optimization (PSO), which is called GSA-PSO. The operation of GSA-PSO...
متن کاملA Short-Term Load Forecasting Model with a Modified Particle Swarm Optimization Algorithm and Least Squares Support Vector Machine Based on the Denoising Method of Empirical Mode Decomposition and Grey Relational Analysis
As an important part of power system planning and the basis of economic operation of power systems, the main work of power load forecasting is to predict the time distribution and spatial distribution of future power loads. The accuracy of load forecasting will directly influence the reliability of the power system. In this paper, a novel short-term Empirical Mode Decomposition-Grey Relational ...
متن کاملLeast Squares Support Vector Machine for Constitutive Modeling of Clay
Constitutive modeling of clay is an important research in geotechnical engineering. It is difficult to use precise mathematical expressions to approximate stress-strain relationship of clay. Artificial neural network (ANN) and support vector machine (SVM) have been successfully used in constitutive modeling of clay. However, generalization ability of ANN has some limitations, and application of...
متن کاملVariance Minimization Least Squares Support Vector Machines for Time Series Analysis
Here we propose a novel machine learning method for time series forecasting which is based on the widelyused Least Squares Support Vector Machine (LS-SVM) approach. The objective function of our method contains a weighted variance minimization part as well. This modification makes the method more efficient in time series forecasting as this paper will show. The proposed method is a generalizati...
متن کاملTime series forecasting of Bitcoin price based on ARIMA and machine learning approaches
Bitcoin as the current leader in cryptocurrencies is a new asset class receiving significant attention in the financial and investment community and presents an interesting time series prediction problem. In this paper, some forecasting models based on classical like ARIMA and machine learning approaches including Kriging, Artificial Neural Network (ANN), Bayesian method, Support Vector Machine...
متن کامل