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monthly load time series 月负荷时间序列
The daily peak-valley load time series, which is proved chaotic by fractal dimension and Lyapunov exponent analysis, is constructed based on the daily load curves. 采用混沌相空间重构理论进行电力短期负荷预测,存在峰谷荷预测精度相对较差和预测参考点不易选取的问题。
The Mackey-Glass time series, the Ikeda map whose parameter values change with time and the power load time series are applied to test this method, respectively. Mackey-Glass 混沌时间序列、变参数的Ikeda 映射时间序列和电力负荷时间序列预测的仿真结果表明该预测方法的有效性。
Short-term Load Time Series Forecasting Based on Correlative Neighboring Points and Peak-valley Correction 基于相关邻近点与峰谷荷修正的短期负荷时间序列预测
Parameter Optimization of Phase Space Reconstruction for Short-term Load Time Series 电力短期负荷时间序列混沌相空间重构参数优选法
Technical improvement of chaotic property analysis to the power short-term load time series 电力短期负荷时间序列混沌特性分析的技术改进
Used for page load time metrics. 设置开始下载计时的时钟。
This is called a deseasonalizing time series. 这叫调和时间数列。
Time Series Analysis Hamilton J.D. 时间序列分析。
The load time of all objects is shown visually with time bars. 加载对象的耗时将通过在时间条来直观地展示。
Predicts the future values for a time series. 预测一个时序的未来值。
Results A new mode of time series is established. 结果建立了一个新的时间序列模型。
The Microsoft Time Series algorithm uses a linear regression decision tree approach to analyze time-related data, such as monthly sales data or yearly profits. Microsoft时序算法使用线性回归决策树方法来分析与时间相关的数据,例如,月销售额数据或年利润。
Breaking life lessens when the maximum load time is longer. 随着最大载荷保持时间的增加，层合板的断裂寿命降低。
The application of the proposed model to predict the monthly runoff shows that it can effectively dealing with the complicated hydrological time series with preferable precision. 实例表明，该模型能较好地处理复杂的水文数据序列，且有较好的预测精度。
FOR THOSE WHO USE DREAMWEAVER CAN USE A TOOL TO CHECK LOAD TIME. 对于使用DREAMWEAVER开拓的人来说，可以使用特定的工具来检测下载时间。
Yes, load times are faster across the board. 切换场景读取时间会更快。
As a matter of fact, the day's and week's return series show noisier than the monthly return series, but the months return series indicate strong fractal time series. 然而中国股市的日和周对数收益率相对月对数收益率则表现出较强的随机序列特征;而月对数收益率则呈现出较强的混沌序列的特征。深综指的分形维数为4.;6而上综指的分形维为3
Tab to display the tree view of the time series model. 选项卡可以显示时序模型的树视图。
Returns predicted future or historical values for time series data. 返回时序数据的将来或历史的预测值。