Weka is a powerful collection of machine-learning software, and supports some time-series analysis tools, but I do not know enough about the field to recommend a best method. However, it is Java-based; and you can call Java code from C/C++ without great fuss.
Packages for time-series manipulation are mostly directed at the stock-market. I suggested Cronos in the comments; I have no idea how to do pattern recognition with it, beyond the obvious: any good model of a length of your series should be able to predict that, after small bumps at a certain distance to the last small bump, big bumps follow. That is, your series exhibits self-similarity, and the models used in Cronos are designed to model it.
If you don't mind C#, you should request a version of TimeSearcher2 from the folks at HCIL - pattern recognition is, for this system, drawing what a pattern looks like, and then checking whether your model is general enough to capture most instances with a low false-positive rate. Probably the most user-friendly approach you will find; all others require quite a background in statistics or pattern recognition strategies.
