采用传统灰色GM(1,1)模型预测道路交通事故这类随机性、波动性较大的数据，存在拟合较差、精度不足等问题。
Random and volatile data of road traffic accidents show poor fitness and low accuracy if forecast by means of the traditional grey model GM (1, 1).
这种模型不适合长期的、随机和波动性较大的数据序列预测。
This model is not suited to forecast the accidents in long term with randomness and great changed data.
特别地，这种模型的预测结果比其它随机波动性较大的数据到模型的预测结果精确得多。
Particularly, the obtained results are more precise than those by other models for data sequences with heaVy random fluctuation.
在此基础上利用随机波动类模型对两种收益率的波动性特征进行拟合，并对拟合优度进行分析。
Based on this we simulate the volatility features of the two kinds of yield rate time series and analyze their fitting results using the stochastic volatility models.
并且以郑州市降雨量的预测作为实例，证明灰色马尔可夫预测模型对于随机波动性较大的数据列的预测具有较高的精度。
The example shows that the grey Markov prediction SCGM(1,1) model can have high prediction precision for the random and fluctuating data series.
研究了负荷时间序列波动性，考虑方差时变特征，提出了基于随机波动（SV）模型的短期负荷预测方法。
The volatility of load time series is analyzed, and the short-term load forecasting based on SV(Stochastic Volatility) models is presented with the consideration of the time-varying characteristics.
研究了负荷时间序列波动性，考虑方差时变特征，提出了基于随机波动（SV）模型的短期负荷预测方法。
The volatility of load time series is analyzed, and the short-term load forecasting based on SV(Stochastic Volatility) models is presented with the consideration of the time-varying characteristics.
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