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

2020 ◽  
Vol 160 ◽  
pp. 113729 ◽  
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

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

2018 ◽  
pp. 302-316 ◽  
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.

scholarly journals Financial time series forecasting using twin support vector regression

PLoS ONE ◽  
2019 ◽  
Vol 14 (3) ◽  
pp. e0211402 ◽  
Author(s):  
Deepak Gupta ◽  
Mahardhika Pratama ◽  
Zhenyuan Ma ◽  
Jun Li ◽  
Mukesh Prasad
Keyword(s):  
Time Series ◽  
Support Vector Regression ◽  
Financial Time Series ◽  
Time Series Forecasting ◽  
Support Vector ◽  
Twin Support Vector Regression ◽  
Financial Time
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