| 1. | The pure time - series analysis was developed firstly
早期的研究主要以时间序列数据为主。 |
| 2. | The evolutionary modeling of higher - order ordinary differential equations for time - series analysis
高阶常微分方程的演化建模用于时间序列的分析 |
| 3. | Aim to propose a new mode of time series based on the traditionally seasonal time - series analysis
摘要目的对传统季节时间序列模型进行分析研究，提出一种新的季节时间序列模型。 |
| 4. | In the oil condition monitoring field of mechanical equipment , the use of the method of time - series analysis has rarely been reported
在机械设备的滑油监控领域，对其光谱数据采用时序分析目前尚不多见。 |
| 5. | During this course we will examine applications of several learning techniques in areas such as computer vision , computer graphics , database search and time - series analysis and prediction
课程期间我们将检视数种学习技巧在一些领域上的应用如电脑视觉、电脑绘图、数据库搜索和时间数列分析与预测。 |
| 6. | During this course we will examine applications of several learning techniques in areas such as computer vision , computer graphics , database search and time - series analysis and prediction
课程期间我们将检视数种学习技巧在一些领域上的应用如计算机视觉、计算机绘图、数据库搜索和时间数列分析与预测。 |
| 7. | Considering the special function of wavelet technology in gross error detection and noise reduction , the paper presents , in improved time - series analysis method based on wavelet noises reduction focused on the combination of wavelet technology and traditional data processing technology , the application of this method for gps deformation monitoring is demonstrated by a practical example
摘要考虑到小波技术在粗差探测、消噪方面有特殊的作用，本文结合实际算例，把小波分析与传统数据处理技术结合作为研究对象，提出一种基于小波消噪的时序分析改进法。 |
| 8. | After studying various constraints , a constraint - based cluster using function partition based on angle dissimilarity , a partition sowntown means with least outspread tree method , and the quantity of customer ' s sale was forecasted by time - series analysis were presented . . so , a series of cspr ( cluster sown town partition rule ) to manage lcc database was describe
为实现对满足约束条件的聚类寻找适当的方法，作者在分析聚类约束条件的基础上，提出运用基于角度相异度的函数划分法处理地理位置的约束条件，用最小生成树聚类分析方法生成满足多项约束条件的聚类，用时间序列预测技术使客户销量约束条件趋于完善。 |
| 9. | The analysis of time - series is important for economics statistics and forecasting . up till now , most documents adopt arima model to carry on modeling and predict to time - series analysis extensively . but arima model needs more than 50 historical statistics in model discerning , and it is difficult to collect data by quarter , month or year
时间序列分析在经济统计与预测中占有重要地位，到目前为止，大多数文献广泛采用arima模型法对时间序列分析进行建模与预测，可是arima模型法在模型识别时需要50个以上历史统计数据，这对按月、按季或按年记录的经济资料往往较难收集。 |
