Soybean price forecasting based on Lasso and regularized asymmetric ν-TSVR

Abstract

Asymmetric ν-twin Support vector regression (Asy-ν-TSVR) is an effective regression model in price prediction. However, there is a matrix inverse operation when solving its dual problem. It is well known that it may be not reversible, therefore a regularized asymmetric ν-TSVR (RAsy-ν-TSVR) is proposed in this paper to avoid above problem. Numerical experiments on eight Benchmark datasets are conducted to demonstrate the validity of our proposed RAsy-ν-TSVR. Moreover, a statistical test is to further show the effectiveness. Before we apply it to Chinese soybean price forecasting, we firstly employ the Lasso to analyze the influence factors of soybean price, and select 21 important factors from the original 25 factors. And then RAsy-ν-TSVR is used to forecast the Chinese soybean price. It yields the lowest prediction error compared with other four models in both the training and testing phases. Meanwhile it produces lower prediction error after the feature selection than before. So the combined Lasso and RAsy-ν-TSVR model is effective for the Chinese soybean price.