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Stock Index Prediction Method based on ARIMA-ELM Combination Model

Stock price prediction has always been a concern in the financial market. This paper combines two models, differential integrated moving average autoregressive model(ARIMA) and the extreme learning machine model(ELM), to predict the closing price of stock indexes. The closing price of the SSE 50 Index is selected as the experimental object, and it is divided into the 80% training set and 20% test set. Firstly, we use the training set data to construct the ARIMA model; then use the fitted values of the ARIMA model, the selected technical indicators, and the trading indicators as the input nodes of the ELM model, and set the number of hidden layer nodes of the different ELM models on the training set. After evaluating the performance of the combination model, the optimal number of hidden layer nodes is selected; finally, the test set data is used to predict the closing price of the index on the constructed combination model, and the effect of the model is evaluated. The simulation experiment results show that the combination model is better than the single ARIMA model or ELM model and has a better prediction effect.