Using support vector machines for time series prediction

In this paper, time series prediction is performed by support vector machines (SVMs), Elman recurrent neural networks, and autoregressive moving average (ARMA) ...

Time Series Forecasting with Support Vector Regression

In conclusion, Support Vector Regression (SVR) offers a robust framework for time series forecasting. By leveraging the power of kernel ...

ML-For-Beginners/7-TimeSeries/3-SVR/README.md at main - GitHub

Regression: Supervised learning technique to predict continuous values from a given set of inputs. · Support Vector Machine (SVM): A type of supervised machine ...

Time series forecast using SVM? - Data Science Stack Exchange

I would like to use SVM to predict the future values of the sie. How can I implement python code to predict these values?

Time Series Forecasting using Support Vector Machine (SVM) in R

Support vector machine is not commonly regarded as the best method for time series forecasting, especially for long series of data.

Time Series Forecasting with Support Vector Regression (SVR)

SVR uses support vectors, which are a subset of the training data points, to define a hyperplane that maximizes the margin around the predicted ...

(PDF) Using support vector machines for time series prediction

Support Vector Machines (SVM) are a relatively new and powerful learner, having attractive characteristics for time series prediction (Muller et al., 1997) .

Time Series Prediction Using Support Vector Machines: A Survey

The underlying motivation for using SVMs is the ability of this methodology to accurately forecast time series data when the underlying system ...

Support vector regression for multivariate time series prediction

In the context of support vector regression, the fact that your data is a time series is mainly relevant from a methodological standpoint ...

Predicting Time Series with Support Vector Machines - Alex Smola

RBF networks and SVR achieve similar results for normal noise. It is to be expected that the method using the proper loss function (squared loss) wins for ...

Predicting time series with support vector machines - SpringerLink

Support Vector Machines are used for time series prediction and compared to radial basis function networks. We make use of two different ...

14: Using Support Vector Machines for Time Series Prediction

Book Chapter 14: Using Support Vector Machines for Time Series Prediction By Klaus-Robert Müller Klaus-Robert Müller Search for other works by this author.

A Comparison of Time Series Forecasting using Support Vector ...

The objective of this study is to examine the flexibility of Support Vector Machine (SVM) in time series forecasting by comparing it with a multi-layer back- ...

Using Support Vector Machines in Financial Time Series Forecasting

This paper investigated the application of SVM in financial forecasting. The autoregressive integrated moving average (ARIMA), ANN, and SVM models were fitted ...

Time series prediction using support vector machines: a survey

The underlying motivation for using SVMs is the ability of this methodology to accurately forecast time series data when the underlying system processes are ...

[PDF] Predicting Time Series with Support Vector Machines

Two different cost functions for Support Vectors are made use: training with an e insensitive loss and Huber's robust loss function and how to choose the ...

SVM kernels for time series analysis - EconStor

Support Vector Machines (SVMs) are a popular tool for the analysis of such data sets. This paper presents some SVM kernel functions and disusses their relative ...

Financial Time Series Forecasting Using Support Vector Machine

In this paper, we transform the financial time series into fuzzy grain particle sequences, and use support vector machine regression to regress the upper and ...

Financial time series prediction using least squares support vector ...

The Bayesian evidence framework is applied in this paper to least squares support vector machine (LS-SVM) regression in order to infer nonlinear models for ...

Support Vector Regression for Non-Stationary Time Series

Their results show that SVM performs better than BP neural network in financial forecasting and possesses comparable generalization performance. 40. Page 62 ...