查找数据趋势的算法? [英] Algorithm for finding trends in data?

问题描述

我正在寻找一种能够找到大量数据趋势的算法.例如，如果给定时间t和变量x，(t,x)并给定输入(例如{(1,1), (2,4), (3,9), (4,16)})，则应该能够确定t=5的x的值是25.通常如何实施?大多数算法是否计算线性，二次方，指数等的最佳拟合线，然后选择标准偏差最小的最佳拟合线?还有其他技术可以找到数据趋势吗?另外，当您增加分析大型向量的变量数量时会发生什么?

I'm looking for an algorithm that is able to find trends in large amounts of data. For instance, if one is given time t and a variable x, (t,x), and given input such as {(1,1), (2,4), (3,9), (4,16)}, it should be able to figure out that the value of x for t=5 is 25. How is this normally implemented? Do most algorithms compute lines of best fit that are linear, quadratic, exponential, etc. and then chooses the line of best fit with the lowest standard deviation? Are there other techniques for finding trends in data? Also, what happens when you increase the number of variables to analyze large vectors?

推荐答案

这是一个非常复杂的问题，请尝试从以下内容开始: http://en.wikipedia.org/wiki/Interpolation

This is a really complex question, try to start from: http://en.wikipedia.org/wiki/Interpolation

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