Carbon future price prediction method and device, computer device and storage medium

Abstract

The invention discloses a carbon price prediction method and device, a computer device and a storage medium. The method comprises the steps that the current energy future price of specified energy isacquired; and the current energy future price is input into a preset carbon future price prediction model based on a least squares support vector machine to carry out price prediction, wherein the kernel function of the carbon future price prediction model is an RBF kernel function. According to the invention, based on the least squares support vector machine, the RBF kernel function is selected to establish the carbon future price prediction model, and then the price of carbon futures is predicted; an inequality constraint in a standard SVM algorithm is replaced by an equality constraint; solving quadratic programming problems is transformed into directly solving linear equations; the method is suitable for price prediction in long, medium and short periods, and has the advantages of non-overlapping reflected information and small training sample; and local minimum points which affect the final prediction result are avoided.

Description

technical field [0001] The invention relates to the field of price forecasting, in particular to a carbon futures price forecasting method, device, computer equipment and storage medium. Background technique [0002] As an emerging financial market, the carbon emissions trading market has developed rapidly in recent years. Carbon trading includes three forms: Clean Development Mechanism (CDM), Joint Implementation Mechanism (JI) and International Carbon Emissions Trading (IET), among which Clean Development Mechanism (CDM) is the most important form of carbon trading in developed and developing countries At present, the channels for China's domestic commercial banks to participate in carbon finance focus on CDM credit investment financing and its intermediary business. There are methods for predicting carbon futures prices in the prior art, but they have disadvantages such as poor long-term forecasting effect, overlapping information reflected by statistical data, and local...

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Problems solved by technology

There is a method for carbon futures price prediction in the existing technology, but it has the disadvantages of poor long-term prediction effect, certain overlapping of information reflected by statistical data, and local minimum points prone to appear in nonlinear modeling.

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