Computational Science and Engineering

Stock price

Engineering

Stock (firearms)

Biology

Support vector machine

Stock market

Time series

Economics

Data mining

Computer science

Geology

Paleontology

Mechanical engineering

Mathematics

Econometrics

Least squares support vector machine

Horse

Artificial intelligence

Series (stratigraphy)

Moving average

Machine learning

Computer vision

Neural Computing and Applications

Stock forecasting is a very complex non-stationary, nonlinear time series forecasting, and is often affected by many factors, making it difficult to predict it with a simple model. Support vector machine (SVM) is one of the common data mining methods in the field of machine learning. It has outstanding advantages compared with other methods and it is widely used in various fields. However, there are still many problems in the practical application of the method, and the model itself has many fields that need to be improved. The purpose of this paper is to accurately predict the trend of stock prices, providing a reference model for the trend of stock market and the tracking method of stock price prediction, and provide value reference for research on the forecasting model of stock market and investor’s investment decision. Research using a combined model to predict stock market trends whether will have a significant improvement compared to using a single model to forecast that. The method of this paper is to analyze the shortcomings of current stock market forecasting methods and standard support vector machines firstly, at the same time, based on this, a cumulative auto-regressive moving average is proposed, which combines the least squares support vector machine synthesis model (ARI-MA-LS-SVM) to make basic predictions for the stock market. Secondly, process the data first for the predictive indicators by using cumulative auto-regressive moving average. Then, use the least squares support vector machine of simple indicator system to predict stock price fluctuations. Therefore, it can be concluded that the combined model based on ARI-MA-LS-SVM is more suitable for stock price forecasting than the single forecasting model, and the actual performance is better. At the same time, a large number of simulation experiments show that the algorithm of multiple model’s fusion can achieve the expected effect, which indicate that the model has universal applicability, market applicability and stability feasibility. This model can bring some guidance and reference value for many investors and market regulators.

Stock price forecast based on combined model of ARI-MA-LS-SVM