Carbon Price Combination Forecasting Model Based on Lasso Regression and Optimal Integration

Abstract

Accurate carbon price index prediction can delve deeply into the internal law of carbon price changes, provide helpful information to managers and decision makers, as well as improve the carbon market system. Nevertheless, existing methods for combination forecasting typically arbitrarily choose a certain set of single forecasting models. However, a particular selection of forecasting models do not apply to all data sets due to the nonlinearity and nonsmoothness of the carbon trading price series. Therefore, choosing suitable single forecasting models for the combination is crucial. Considering the limitations of the current study, this study constructs a combined carbon trading forecasting model based on Lasso regression and optimal integration. By invoking the Lasso regression model, we can select suitable single forecasting models for combination forecasting based on the variation patterns of different training sets. Meanwhile, ARIMA, NARNN, LSTM, and 11 other single forecasting models are screened in this study, including both traditional statistical forecasting models and artificial intelligence forecasting models. First, the carbon price index is predicted using 11 single prediction models. Furthermore, given the multi-collinearity of the single prediction series, this study employs Lasso regression to reduce the dimensions of the single prediction models, which are then used to construct an optimal combination prediction model. Finally, the proposed model is applied to SZA-2017 and SZA-2019 carbon price data in Shenzhen. The results demonstrate that the model developed in this study outperforms other benchmark prediction models in terms of prediction error and direction accuracy, showing the efficacy of the proposed method.