Recurrence plots

Recurrence plots are interesting - see this link for a very good Web site that explains them and what they can be used for. In particular, see this link for an animation that explains how they are made.

Inspired by this I made an example using RapidMiner. It works as follows

1. A simple sine series is made.
2. The "Windowing" operator converts the series into a set of triplets
3. The "Multiply" operator makes a copy of the example set
4. The "Cross Distances" operator calculates the distances between all the points in the two copies
5. The "Filter Examples" operator allows filtering to be done. The smaller the distance the closer the points.

To see the result use the Block plotter and you should get this rather nice looking picture.

For fun, I downloaded the Google share price and I made this even nicer looking picture (this shows distances less than 0.1)

I suspect that when it comes to the application of recurrence plots to share prices there is more money in the art of looking rather than the science of predicting.

Care needed using the Cross Distances operator

The "Cross Distances" operator is used to determine which examples in one set are closest to those in another.

Here's an example correctly showing the first example from the first set at zero distance from the first example in the second set. In other words, these two examples match.

Here's another example that behaves unexpectedly. In this case, the first example in the first set is shown at zero distance from the first example of the second set even though the attribute names and values do not match.

The reason for this is the order of the attributes in the example sets. The attributes are created in different orders and it is this that determines which attribute pairs are compared when calculating distances. You can see this ordering if you go to the meta data view and show the 'Table Index' column. The special attributes are also important.

Normally, most will find that this is not a problem. However, if you import data from two different sources that are supposed to represent the same data but which have columns in different orders, you will find that the "Cross Distances" operator will not behave as expected.

It is possible to work round this by using the "Generate Attributes" operator to recreate attributes in both example sets in the same order.