Hi ggggg You and I have discussed this privately and decided to work up a DynamicTimeWarp package in Julia ourselves, because we couldn't find one. It's not nearly ready for real-world use, I think, but it can be found at GitHub: https://github.com/joefowler/DynamicTimeWarp.jl
Our goals for the package include: 1. Performing Dynamic Time Warping between 2 time series. 2. Performing DTW with the solution path restricted to a specified "window". This restriction speeds up the computation but can fail to find the global optimum. 3. The FastDTW algorithm (Salvador & Chan, 2007), which effectively chooses a window by downsampling the original problem and running FastDTW on that (or as a base case, running DTW once the down sampled problem is small enough). Also faster but potentially misses the full DTW solution. 4. DTW Barycenter Averaging (Petitjean, Ketterlin, and Gancarski, _Pattern Recognition_ 44, 2011). This algorithm aims to create a "consensus sequence" iteratively from 2 or more sequences, using the identification of samples that DTW generates between the latest consensus and the constituent sequences. 5. Tools for using DTW to align spectra. In our work, this would mean calibration to unify uncalibrated energy spectra from x-ray spectrometers. This is not a well-defined goal yet, but it's the reason that you and I actually care about DTW. 6. Demonstrations, documentation, good tests, and the usual things like that. Peak-finding, e.g. by continuous wavelet transforms or any other method, is a separate issue. --Joe On Wednesday, December 3, 2014 4:03:41 PM UTC-7, ggggg wrote: > > Hello, > > I'm interested in using dynamic time warping and an algorithm for peak > finding in noisy data (like scipy.signal.find_peaks_cwt). I'm just > wondering if there are any Julia implementations around, otherwise I'll > probably just use PyCall for now to use existing python code. > > > > >
