图相关性发现算法
我不是数学家，所以我会尝试用外行人的术语来描述这一点。
我试图采用两个时间序列，它们可以代表任何变量、每日最高温度、当天的股价最高点等。这些将乘以一个系数，使它们的最大值和最小值相对应。 （例如，两个温度系列可能介于不同的最冷温度和最热温度之间，但在这两个温度系列中，我将最冷的温度视为 0%，将最热的温度视为 100%。）
鉴于此，我想找出它们的开始时间的相对变化会产生什么“最”相关。即具有“高”相关性的最长采样周期。 （我知道这有点模糊。）
举个简单的例子，考虑到去年几个城市的气温，它可能会选择两个城市，这两个城市都有几周的时间段，其中每隔一天的最高气温是 2/3。前一天。两个城市不一定在同一天开始。这就是时移试验的用武之地。
指向讨论、伪代码或实际实用程序库的指针会很好。
I'm not a mathematician, so I'll try to describe this in a layperson's terms.
I'm trying to take two time series, which could represent any variable quantity, maximum daily temperature, stock price high of the day, etc. These would be multiplied by a factor that would make their maxima and minima correspond. (E.g., two temperature series might range between different coldest and warmest temperatures, but in both I'd treat coldest as 0% and warmest as 100%.)
Given this, I want to find out what relative shift in their start times would produce the "most" correlation. That is, the longest sample period with a "high" correlation. (I know that's a bit fuzzy.)
As a simple example, given last year's temperatures for several cities, it might choose two cities that both had a period of several weeks in which every other day had a maximum temperature that was 2/3 of the preceding day. This didn't necessarily start for both cities on the same day. That's where the time shifting trials come in.
A pointer to a discussion, pseudo code, or actual utility library would be good.
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您正在尝试计算交叉相关性。
You are trying to calculate Cross-Correlations.