Because traditional time series analysis of stock price lacks the crucial factor-trading volume, we suggest that price movements evolve on the trading volume.
本文认为基于固定时间推进的股价时间序列分析缺少考虑成交量的重要影响，提出基于成交量过程推进的股价变化模型。
On the basis of traditional time series analysis and modeling methods, the thesis puts forward a new complete and simply identification method by using ar model.
本文在传统时间序列分析建模方法的基础上，提出了用AR模型的新的完整而又简单的辨识方法。
A forecast instance indicates that compared with the traditional time series analysis, the present forecast method can carry out more accurate forecast of vibration response trends.
预测实例表明，相比于传统的时间序列分析方法，这种预测方法能对振动响应的趋势进行更准确的预测。
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