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rowavedt : Robust (Semi-parametric) Wavelet-based Event Detection

This package provides the core routines from Blocker and Protopapas 2012 (arXiv:1301.3027 [stat.AP]). It includes the PX-EM algorithm for estimation of the semi-parametric model presented in that paper, as well as R scripts for the construction of the wavelet basis, frequency-based screening for time series with interesting variation, and construction of features for subsequent classification.

Code for the training and use of the classifier is not included. The former used existing techniques from the arm package for R in combination with scientific simulations (which are not easily generalized to other uses). The latter is straightforward, once the features are constructed and the classifier is trained.


This package is written for *nix systems, although it may work on Mac as well (untested). The core estimation code (written in C) requires a BLAS library with LAPACK configured and the GNU Science Library. The ATLAS and Intel MKL BLAS libraries have been tested successfully, and compilation options for both are included in the Makefile. Compilation requires gcc and glibc.

All R scripts require the optparse package. The script to construct wavelet basis matrices (mk_wavelet_basis.R) requires the wavethresh package as well.


To compile and install the core estimation routine (written in C), first edit the included Makefile as needed. The entries that may require modification are:

  • INSTALLDIR: Directory that binary will be copied to by make install
  • LIBS: Libraries included in compilation and linking. Standard entries for ATLAS and LAPACK are included.
  • INCLUDES: Include options for header files. Default for GSL and ATLAS are included.
  • CFLAGS: Additional arguments to pass to gcc. Defaults to -O3 -Wall

Next, running make all from the package's root directory will produce the rowavedt binary. To test this using the data provided in the data directory, run make test. This will produce test/output.txt.

Finally, to install the rowavedt binary to INSTALLDIR, run make install (as root, if necessary).

The R scripts mk_wavelet_basis.R, screen_time_series.R, and compute_features.R can all be used directly from the scripts/ directory via Rscript.


The workflow is: construct a basis matrix using mk_wavelet_basis.R, use this basis matrix to fit the semi-parametric model to the time series of interest using rowavedt (likely with using a customized wrapper script for your dataset), then screen for variation at interesting scales using screen_time_series.R.

All scripts and files have help and usage information available with the -h command line option. A few key points:

  • Inputs to rowavedt need to be space-delimited text. For the time series files (DATAFILE), the default is to have observation times in the first column and observed values in the second. These can be changed via the -t and -c options, respectively.
  • rowavedt and mk_wavelet_basis.R write their output to stdout, but screen_time_series.R writes its output to two files specified as arguments to accommodate a separate output for detection statistics.
  • compute_features.R requires both the output from rowavedt and the basis matrix as inputs. If you use the --detections option with this script, be careful to sort both the detections file (as output by screen_time_series.R) and the output from rowavedt by their ID column. If you sort the output from rowavedt before sending it to screen_time_series.R, the former will be automatic.


Robust wavelet-based semi-parametric event detection.







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