如何找到顶部和底部的时间序列？ [英] how to find tops and bottoms in time series?

问题描述

首先，这个问题听起来很愚蠢，但它不是根本。也许，这似乎是无法解决的完全由任何算法，但我pretend说，这是。

于是问题。我有图表，例如黄金。我需要找到在顶部和底部的时间轴。问题是我需要找到大落，大衰退开始。问题是，有很多的小不相关的经济好转和经济衰退。

下面的图片为更好的理解 - 红点是，我想找到（不完全，但在某些方面是这样）。

所以，我可能需要过滤掉小的卷起部分和量程比，但不知道该怎么做。我会很高兴的任何想法。我不需要算法的java等，只是在口头上就足够。谢谢

解决方案

1. 您可以先执行平滑或低通滤波运行，并找到局部最小值/最大值从平滑后的数据的位置。然后得到的最小值和从原始数据的极大值的值。

2. 您可以使用普通的最高/最低过滤器，发现的所有的转折点，然后通过筛选门槛转折点的列表。

3. 我想你的真的想要做的是去除信号的长期变化，并期待仅在短期内的变化。这可以通过使用经验模式分解。参见2.3.2节<一href="http://www.penwatch.net/files/thesis/Online_Empirical_Mode_Decomposition_Li-aung_Yip_2010.pdf">my论文。（Alernately，谷歌四处经验模式分解，EMD，或希尔伯特 - 黄变换。）

这里的EMD在行动：

请注意，增加一般性的EMD算法提取信号的分量，开始在最详细，并以最大势所趋的结局。 （注意，有明显九个部分 - 只有少数显示）

At first, this question can sound really stupid, but it is not in fundamental. Maybe, it can seem like unresolvable exactly by any algorithm, but i pretend to say it is.

So question. I have chart, for example gold. I need to find where are tops and bottoms on time axial. The problem is I need to find where major upturns and major downturns start. The problem is that there is lot of small irrelevant upturns and downturns.

Here is the picture for better understanding - the red spots are that I want to find(NOT EXACTLY, but in some way like this).

So I probably need to filter out small turnups and turndowns, but have no idea how to do it. I will be pleased by any ideas. I do not need algorithm in java etc, just in words it would be enough. Thank you

解决方案

1. You could perform a smoothing or lowpass filtering operation first, and find the locations of the local minima/maxima from the smoothed data. Then get the values of the minima and the maxima from the original data.

2. You could use a normal maximum/minimum filter, which finds all turning points, then filter the list of turning points by threshold.

3. I think what you really want to do is remove the "long-term variation" from the signal and look only at the "short term variation". This is can be done using the empirical mode decomposition. See Sec 2.3.2 of my thesis. (Alernately, Google around for "Empirical Mode Decomposition", "EMD", or "Hilbert-Huang Transform".)

Here's the EMD in action:

Notice the increasing generality as the EMD algorithm extracts components of the signal, starting at "most detailed" and ending with "most general trend". (Note there are apparently nine components - only a few are shown.)

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