时间序列模型的改进与应用 Alternative Title The Improvement and Application of Time Series Models 董瑶 Thesis Advisor 王建州 2012-06-02 Degree Grantor 兰州大学 Place of Conferral 兰州 Degree Name 硕士 Keyword 时间序列模型 季节调整 经验模式分解法 预测 Abstract 时间序列模型是依照动态数据反映系统动态结构和规律的数学模型。然而，在实际应用中，时间序列模型预测的效果并不是很理想。为了提高预测精度，本文根据两个实例中观测数据的特点，对时间序列模型进行改进。 在风力发电系统中，风速是一个重要的参数。本文提出第一个改进的时间序列模型进行长期的风速预测。首次使用先定季节指数法对原始数据进行季节调整，其次确定趋势项，利用ARMA模型或GARCH模型。本文将预测结果与ARIMA模型进行比较，模拟过程和实验结果显示：这种改进模型是预测河西走廊张掖市每天平均风速简单有效的方法。 在电力系统中，每小时电价具有波动性。电价的波动性受很多因素的共同影响。因此，使用传统单一的模型预测不同行为模式的每小时电价是非常困难的。本文提出第二个改进的时间序列模型，此模型应用经验模式分解法（EMD）进行去噪处理分离高频，再使用季节调整剔除季节项的影响，最后利用ARIMA模型进行预测。通过与传统的季节ARIMA模型相比较，提出的改进模型能够很好地提高预测精度。 Other Abstract Time series models are mathematical models of reflecting system dynamic structure and laws. However, time series models are not very satisfactory. In order to improve accuracy, according to the characteristics of the observed data, two times series models can be improved. Within a wind energy system, the wind speed is one key parameter. This paper proposes a improved time series model for long-term wind speed forecasting based on the first definite season index method and ARMA model or GARCH model. The simulation results show that the developed method is simple and quite efficient for wind speed forecasting compared with ARIMA model. Half-hourly electricity price in power system are volatile. However, the fluctuation depends on many factors. Therefore, it is difficult to use only one model. This paper proposes the second improved time series model that detaches high volatility and daily seasonality for electricity price of New South Wales in Australia based on EMD, Seasonal Adjustment and ARIMA model. The results demonstrate that the proposed model can improve the prediction accuracy noticeably compared with Seasonal ARIMA model. URL 查看原文 Language 中文 Document Type 学位论文 Identifier https://ir.lzu.edu.cn/handle/262010/224711 Collection 数学与统计学院 Recommended CitationGB/T 7714 董瑶. 时间序列模型的改进与应用[D]. 兰州. 兰州大学,2012.
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