水文水资源序列是一个具有周期变化、随机变化和递增或递减趋势变化的复杂时间序列。
Hydrologic sequence is a complicated time sequence, which has characteristics such as periodic changes, random changes and increasing or decreasing trend changes.
针对复杂时间序列难以使用单一预测方法进行有效预测的问题，本文提出一种新型多分辨率增量预测模型。
The task of complex time series predicting is hard to be accomplished with only one single predicting model.
脚本可以节省重新输入复杂的较长命令序列所需的时间和精力，并且还可以防止发生错误。
A script saves the time and energy required to retype complex and long sequences of commands, preventing mistakes, too.
在我们身体中许多基因控制着蛋白质编码基因在不同地方和时间的开和关，这就让基因序列更加复杂了。
Many genes control when protein-coding genes are turned on and off at different places and times in the body, adding a whole new layer of complexity to the genome.
复杂图形的例子包括专业化的条形图和须状图，带有完整误差的时间序列条形图，颜色编码和密度图，以及许多其他可能的图。
Examples of complicated graphs include specialized bar-and-whiskers plots, time-series with sophisticated error bars, color encodings and density plots, and many other possibilities.
城市燃气负荷是一个多工况、复杂的工程系统，本文采用时间序列方法对深圳的燃气负荷进行了分析和预测。
City gas load is a multi-mode and complicated engineering system. This paper analyzes and estimates the gas load of Shenzhen by using method of time series analysis.
医院门诊量是一个具有复杂的非线性组合特征的季节性时间序列。
The hospital outpatient amount is a seasonal time series with the character of complex non-linear combination.
医院月门诊量是一个具有复杂的非线性组合特征的季节性时间序列。
The hospital outpatient amount is a seasonal time series with the character of complex nonlinear combination.
作为一类重要的复杂类型数据，时间序列已成为数据挖掘领域的热点研究对象之一。
As one of the important forms of complex data, time series is a hotspot in data mining area.
混沌和支持向量机理论为研究复杂多变的非线性水文时间序列开辟了新的途径。
Chaos and support vector machine theory has opened up a new route to study complicated and changeable non-linear hydrology time series.
零散资料具有篇幅小、涉及内容广泛、时间序列长等特点，而且来源复杂，存储形式多样。
The scattered resources have the characteristics such as small length, content widely, long time series and so on, moreover complex sources and various formats.
对几种特殊的输入函数进行了编码仿真实验，结果说明了视皮层复杂细胞时空整合编码序列的精细时间结构能够进行视觉输入的神经表象。
The simulated results for three special input functions show the finer structure of spatiotemporal integration coding series of complex cells in visual cortex could represent visual inputs.
医院月门诊量是一个具有复杂的非线性组合特征的季节性时间序列。
The hospital outpatient amount is a seasonal time series with the character of complexive nonlinear combination.
用支持向量回归（SVR）的方法分析和预测时间序列，可解决复杂非线性系统的建模问题。
The Support Vector Regression(SVR)is used for the time series analysis and prediction to resolve the complex nonlinear system modeling problems.
分析了事务与关联规则在二进制序列集中的表示方法及其在空间、时间上的复杂度。
It analyzes the express method of affair and association rule in the binary system sequences set and complexity in space and time.
这一概念是对线性偏自相关的一般化，由它可以得到度量时间序列预测复杂性的定量方法。
By means of it, we could get the quantitative method to measure the intrinsic prediction complexity of time series.
利用混沌序列的遍历性及其良好的相关特性，提出了一种二维置换网络，并对置换网络的时间复杂度和其置换性质做了分析。
Put forward a planner permutation network by using the ergodic and good relativity of Chebyshev-map chaos sequence, and the time complexity and analyzed permutation character of permutation network.
这一概念是对线性偏自相关的一般化，由它可以得到度量时间序列预测复杂性的定量方法。
The concept is the generalization of partial autocorrelation. By means of it, we could get the quantitative method to measure the intrinsic prediction complexity of time series.
因为对二元互补序列偶的搜索涉及到四个序列的计算，搜索的时间复杂度和空间复杂度很大。
Time complexity and space complexity in searching binary complementary sequence pair are very huge because four sequences are involved in the calculation.
在对生物医学信号时间序列进行复杂度分析时，往往需要首先对原始信号进行粗粒化预处理。
It is often to take a coarse graining preprocessing before a complexity analysis is made for biomedical signal time series.
由于干旱地区气候干燥、降水稀少、蒸发强烈，使得水文过程呈现出非常复杂的变化过程，水文时间序列表现出高度的非线性和不确定性。
In this region, dry climate, rare rain and strong evaporation make hydrological process show very complex change process, hydrological time series present highly nonlinear and uncertainty.
它是由一个简单的表达式语言驱动的，你用来检索时间序列数据，进行计算，找出复杂的问题的答案，并可视化的结果。
It's driven by a simple expression language you use to retrieve time series data, perform calculations to tease out the answers to complex questions, and visualize the results.
而在这其中时间序列数据挖掘是面向特殊应用数据挖掘领域中比较复杂的一个分支，主要研究从大量时间序列历史数据中挖掘有价值信息的方法和相关技术。
Among these research fields, time series data mining is a rather complex branch, which is a technique that extracts the most valuable information from large amount of history time series data.
然而多元时间序列的数据结构比一元时间序列更复杂，现有的理论和方法仍不够完善。
However, there are a few on multivariate time series mining, since the data structure of multivariate time series is more complex than that of univariate time series.
证明了基于并行计算的线性空间算法的在进行长序列比对时，时间复杂性优于经典的线性空间算法。
It testified that linear space algorithm based on parallel computing is superior to classical linear space algorithm in time complexity.
时间序列数据是一种复杂的数据对象，在社会生活中的各个领域广泛存在着大量的时间序列数据有待于进一步的分析和处理。
Time series is very important and complicated data object. There are a lot of time series need to be further analyzed and processed in the all kinds of areas in society.
该方法通过对时间序列排序模式进行分类，来实现复杂的概率分布估计，从而直接估计出时间序列的信息量。
The proposed method calculates the probability distribution of time series based on the classification of order patterns to directly estimate the amount of information in time series.
股票市场是一个复杂的非线性动态系统，利用传统的时间序列预测技术预测效果不理想。
As stock market is a kind of complex non-linear dynamic system, the prediction results of traditional prediction technology are unsatisfactory.
由于检测对DC序列进行，算法计算时间复杂度比较低。
Since the detection is carried out on DC sequence, the computation complexity of this method is relatively low.
这有利于程序处理非常复杂，分支，循环和长时间序列的可能性。
This facilitates the possibility for the program to deal with very complicate, branched, cyclic and lengthy sequences.
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