Forecasting Inflation Rate of Zambia Using Holt’s Exponential Smoothing

In this paper, the Holt’s exponential smoothing and Auto-Regressive Integrated Moving Average (ARIMA) models were used to forecast inflation rate of Zambia using the monthly consumer price index (CPI) data from May 2010 to May 2014. Results show that the ARIMA ((12), 1, 0) is an adequate model which best fits the CPI time series data and is therefore suitable for forecasting CPI and subsequently the inflation rate. However, the choice of the Holt’s exponential smoothing is as good as an ARIMA model considering the smaller deviations in the mean absolute percentage error and mean square error. Moreover, the Holt’s exponential smoothing model is less complicated since you do not require specialised software to implement it as is the case for ARIMA models. The forecasted inflation rate for April and May, 2015 is 7.0 and 6.6 respectively.

基于区间时间序列小波多尺度分解的组合预测方法

针对非线性、非平稳且呈现剧烈波动的区间时间序列,文章提出了一种新的基于区间时间序列小波多尺度分解的组合预测方法。首先,建立区间时间序列小波多尺度分解模型对区间时间序列进行分解和重组,得到区间趋势序列与残差序列。然后,用Holt’s指数平滑方法、ARIMA模型和支持向量回归(SVR)三种单项预测方法对分解后的趋势序列和残差序列进行预测,再通过BP神经网络对单项预测结果进行集成,得到区间时间序列最终预测值。最后,将本模型应用于WTI原油价格的实证分析中,结果表明,相比已有的预测方法,所提出的区间时间序列组合预测方法具有较高的预测精度和良好的适用性。

融合量测重构的RGAN-UKF智能电网状态估计模型

针对电力系统量测数据不完整导致状态估计失效进而危害智能电网安全经济运行的问题,本文提出一种融合量测重构的残差生成对抗网络-无迹卡尔曼滤波状态估计方法,以进行电力系统状态估计。首先,分析模型重构量测的基本工作原理,以重构不完整量测数据;然后,结合Holt’s双参数平滑法与无迹卡尔曼滤波方法,构造了融合重构量测的动态估计方法;最后,在IEEE 30节点系统进行仿真,验证了所提方法的可行性和有效性。