I modified previously provided tools to build a new one which gets both Training and test datasets of a time series to see how much the given test dataset satisfies the learnt pattern (or simply validates the given test set). If you need to get more information on Bayesian Network, the following link shows almost all I've written on this topic.
The tool is at the following address in the blog tools section:
Simple time series pattern recognition
You may like to test it with the default provided data first, but we are going to describe how it works and how you can use it. So forget about the default data in the text areas, copy and paste the following data in first text area training and let the second one be empty:
|Single period of the training data|
|Three periods of training data|
|Three training dataset and one test dataset|
How does it work?
We have talked about it before; the short story is that the tool builds a Bayesian Network from the given training datasets. To convert datasets to nodes and edges in the network, it lowers the resolution of the data for both time and value of the series and connects time nodes to corresponding value nodes, increases if the connection already exists, then it calculates probabilities. Here is the network the tool generates for the above test.
|Bayesian Network for the above example|
It is all up to you, the tool gives you the probabilities, you can assume having one zero probability means the test data doesn't match the trained pattern. You can have some tolerance and say having 90% none zero probability is OK. Or even you may say I only accept the points with a probability of higher than 0.25 so if you have one lower than 0.25 you should reject or invalidate the test data.