时序数据 meaning in Chinese
data, timing
time series data
Examples
- The time serial of short fatigue cracks ' propagation rate is modeling and predicting by using neural network in this article , and the good result is achieved
文中利用神经网络对短裂纹扩展速率进行时序数据建模与预测，取得较好的效果。 - Moreover , fuzzy sets theory is adopted in the dissertation to deal with the uncertainty of the mining process and a new fuzzy frame of tsdm is given then
另外，本文用模糊集理论对时间序列数据挖掘过程中的不确定性进行了处理，提出了一种模糊时序数据挖掘的框架。 - Because of the growing of time - series database and the potential significance of data mining , the research of data mining in time - series database has become a hotspot . at the same time , however , the nonlinear and chaotic characteristic of time - series data makes the mining be a difficult issue
由于时间序列数据库的日趋庞大及其挖掘的潜在意义，目前，时序数据挖掘研究已成为一个热点；然而,时间序列数据的非线性混沌特点，使得对它的挖掘成为难题。 - The detailed works are as follows : the finding patterns problems in the time - series data sequence are described , and a new trend logic expression method is introduced , and its algorithm and experiment result of algorithm are given ; time - scries data are disposed , and using the arctg . slope of line as the sample of pattern recognition , so ignoring the aberrance of pattern in the classified . in addition , a new time - series pattern finding algorithm based on higher - order neural network is put forward
同时给出了本文的具体的工作，主要是：对在时序数据序列中发现模式问题进行了描述，并介绍了一种新的趋势逻辑表示方法，给出了其算法及算法的实验结果；对时序数据进行处理，提出了利用线段的斜率反正切值作为模式识别的样本，从而在分类时忽略模式的畸变；另外，还提出了一个新的基于高阶神经网络的时序模式发现算法。 - Time series is a kind of important data existing in a lot of fields , such as stock , weather , etc . with time moving , this data of time series will explode increasing . so it is important and challenging subject to research how discovery valuable knowledge in large - scale time series database , and how to search based similarity while user give a graphic query pattern
因此，对这些海量的时序数据如何进行有效的知识发现，挖掘其内在的各种变化模式：对于用户给定具有各种抽象含义的变化模式，如何在海量时间序列库中进行相似性的检索等应用分析，这是一个挑战性的、具有重要意义的理论和实际应用课题。