Nasdaq and Shanghai Composite Index Forecast Based on ARIMA and ETS Models

Accurately predicting asset prices is crucial for investors, as it not only helps them avoid risks and seize market opportunities, but also influences economic trends, optimizes resource allocation, and ensures the scientific and effective nature of investment decisions. In this paper, ARIMA model and ETS model are used to predict the time series of NASDAQ index and Shanghai Composite Index respectively. By comparing the prediction performance indexes of the two models, such as mean square error (MSE) and root mean square error (RMSE), it is found that ARIMA model has higher prediction accuracy in most cases. Besides, the results show that the performance of different models in different markets is different, which provides a basis for investors to choose the right forecasting model. This study provides valuable empirical analysis for the field of asset price forecasting, especially in the context of global economic uncertainty, and has important practical application value.