A Forecasting Methodology Using Support Vector Regression and Dynamic Feature Selection

Various techniques have been proposed to forecast a given time series. Models from the ARIMA family have been successfully used, as well as regression approaches based on e.g. linear, non-linear regression, neural networks, and Support Vector Regression. What makes the difference in many real-world applications, however, is not the technique but an appropriate forecasting methodology. Here, we propose such a methodology for the regression-based forecasting approach. A hybrid system is presented that iteratively selects the most relevant features and constructs the regression model optimizing its parameters dynamically. We develop a particular technique for feature selection as well as for model construction. The methodology, however, is a generic one providing the opportunity to employ alternative approaches within our framework. The application to several time series underlines its usefulness.

Download Full-text

Forecasting Demand With Support Vector Regression Technique Incorporating Feature Selection in the Presence of Calendar Effect

Contemporary Approaches and Strategies for Applied Logistics - Advances in Logistics, Operations, and Management Science ◽

10.4018/978-1-5225-5273-4.ch012 ◽

2018 ◽

pp. 302-316 ◽

Cited By ~ 7

Author(s):

Malek Sarhani ◽

Abdellatif El Afia

Keyword(s):

Time Series ◽

Feature Selection ◽

Supply Chains ◽

Support Vector Regression ◽

Case Studies ◽

Time Series Forecasting ◽

Regression Technique ◽

Support Vector ◽

Reliable Prediction ◽

Forecasting Methods

Reliable prediction of future demand is needed to better manage and optimize supply chains. However, a difficulty of forecasting demand arises due to the fact that heterogeneous factors may affect it. Analyzing such data by using classical time series forecasting methods will fail to capture such dependency of factors. This chapter addresses these problems by examining the use of feature selection in forecasting using support vector regression while eliminating the calendar effect using X13-ARIMA-SEATS. The approach is investigated in three different case studies.

Download Full-text

SVR-FFS: A novel forward feature selection approach for high-frequency time series forecasting using support vector regression

Expert Systems with Applications ◽

10.1016/j.eswa.2020.113729 ◽

2020 ◽

Vol 160 ◽

pp. 113729 ◽

Cited By ~ 2

Author(s):

José Manuel Valente ◽

Sebastián Maldonado

Keyword(s):

Time Series ◽

Feature Selection ◽

Support Vector Regression ◽

High Frequency ◽

Time Series Forecasting ◽

Support Vector ◽

Selection Approach ◽

Feature Selection Approach

Download Full-text

A study on the ability of Support Vector Regression and Neural Networks to Forecast Basic Time Series Patterns

IFIP International Federation for Information Processing - Artificial Intelligence in Theory and Practice ◽

10.1007/978-0-387-34747-9_16 ◽

2006 ◽

pp. 149-158 ◽

Cited By ~ 7

Author(s):

Sven F. Crone ◽

Jose Guajardo ◽

Richard Weber

Keyword(s):

Neural Networks ◽

Time Series ◽

Support Vector Regression ◽

Support Vector

Download Full-text

Forecasting with Computational Intelligence - An Evaluation of Support Vector Regression and Artificial Neural Networks for Time Series Prediction

The 2006 IEEE International Joint Conference on Neural Network Proceedings ◽

10.1109/ijcnn.2006.247299 ◽

2006 ◽

Cited By ~ 5

Author(s):

S.F. Crone ◽

S. Lessmann ◽

S. Pietsch

Keyword(s):

Neural Networks ◽

Time Series ◽

Artificial Neural Networks ◽

Support Vector Regression ◽

Computational Intelligence ◽

Time Series Prediction ◽

Support Vector ◽

Artificial Neural

Download Full-text

A combined model for short-term wind speed forecasting based on empirical mode decomposition, feature selection, support vector regression and cross-validated lasso

PeerJ Computer Science ◽

10.7717/peerj-cs.732 ◽

2021 ◽

Vol 7 ◽

pp. e732

Author(s):

Tao Wang

Keyword(s):

Time Series ◽

Feature Selection ◽

Wind Speed ◽

Support Vector Regression ◽

Prediction Performance ◽

Support Vector ◽

Short Term ◽

Combined Model ◽

Wind Speed Forecasting ◽

Mode Decomposition

Background The planning and control of wind power production rely heavily on short-term wind speed forecasting. Due to the non-linearity and non-stationarity of wind, it is difficult to carry out accurate modeling and prediction through traditional wind speed forecasting models. Methods In the paper, we combine empirical mode decomposition (EMD), feature selection (FS), support vector regression (SVR) and cross-validated lasso (LassoCV) to develop a new wind speed forecasting model, aiming to improve the prediction performance of wind speed. EMD is used to extract the intrinsic mode functions (IMFs) from the original wind speed time series to eliminate the non-stationarity in the time series. FS and SVR are combined to predict the high-frequency IMF obtained by EMD. LassoCV is used to complete the prediction of low-frequency IMF and trend. Results Data collected from two wind stations in Michigan, USA are adopted to test the proposed combined model. Experimental results show that in multi-step wind speed forecasting, compared with the classic individual and traditional EMD-based combined models, the proposed model has better prediction performance. Conclusions Through the proposed combined model, the wind speed forecast can be effectively improved.

Download Full-text

Forecasting with Computational Intelligence - An Evaluation of Support Vector Regression and Artificial Neural Networks for Time Series Prediction

The 2006 IEEE International Joint Conference on Neural Network Proceedings ◽

10.1109/ijcnn.2006.1716528 ◽

2006 ◽

Author(s):

S.F. Crone ◽

S. Lessmann ◽

S. Pietsch

Keyword(s):

Neural Networks ◽

Time Series ◽

Artificial Neural Networks ◽

Support Vector Regression ◽

Computational Intelligence ◽

Time Series Prediction ◽

Support Vector ◽

Artificial Neural

Download Full-text

System for Forecasting COVID-19 Cases Using Time-Series and Neural Networks Models

Engineering Proceedings ◽

10.3390/engproc2021005046 ◽

2021 ◽

Vol 5 (1) ◽

pp. 46

Author(s):

Mostafa Abotaleb ◽

Tatiana Makarovskikh

Keyword(s):

Neural Network ◽

Neural Networks ◽

Time Series ◽

Time Series Models ◽

Third Wave ◽

The Third ◽

Modeling And Forecasting ◽

Auto Regressive ◽

The Russian Federation ◽

R Programming

COVID-19 is one of the biggest challenges that countries face at the present time, as infections and deaths change daily and because this pandemic has a dynamic spread. Our paper considers two tasks. The first one is to develop a system for modeling COVID-19 based on time-series models due to their accuracy in forecasting COVID-19 cases. We developed an “Epidemic. TA” system using R programming for modeling and forecasting COVID-19 cases. This system contains linear (ARIMA and Holt’s model) and non-linear (BATS, TBATS, and SIR) time-series models and neural network auto-regressive models (NNAR), which allows us to obtain the most accurate forecasts of infections, deaths, and vaccination cases. The second task is the implementation of our system to forecast the risk of the third wave of infections in the Russian Federation.

Download Full-text