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A fast and easy to use implementation of the spectral envelope method, used in categorical data analysis. This module is now part of the package CategoricalTimeSeries.jl
The spectral envelope is a tool to study cyclic behaviors in categorical data. It is more efficient than the traditional approach of attributing a different number to each category before computing the power-spectral density.
For each frequency in the spectrum, the spectral envelope finds an optimal real-numbered mapping that maximizes the power-spectral density at this point. Hence the name: no matter what mapping is choosen for each category, the power-spectral density will always be bounded by the spectral envelope.
The spectral envelope was defined by David S. Stoffer in DAVID S. STOFFER, DAVID E. TYLER, ANDREW J. MCDOUGALL, Spectral analysis for categorical time series: Scaling and the spectral envelope.\
The main function is:
spectral_envelope(ts; m = 3)
Input
-ts : Array containing the time series to be analysed.
-m : Smoothing parameter. corresponds to how many neighboring points
are to be involved in the smoothing (weighted average). Defaults to 3.
Returns
-freq : Array containing the frequency of the power-spectrum (or spectral envelope)
-se : Values of the spectral envelope for each frequency in 'freq'.
-eigvec : Array containing the optimal real-valued mapping for each frequency point.
-categories : the categories which are present in the data.
To use the spectral envelope, call the function spectral_envelope, you can then easily plot the results and extract the mapping for a given frequency.
Here is an example with DNA data from a portion of the Epstein virus:
using DelimitedFiles, Plots
# extracting data
data = readdlm("..\\test\\DNA_data.txt")
# spectral envelope analysis
f, se, eigvecs = spectral_envelope(data; m = 4)
# plotting the results
plot(f, se, xlabel = "Frequency", ylabel = "Intensity", title = "test data: extract of Epstein virus DNA", label = "spectral envelope")
To get the optimal mappings for a given frequency, you can use the get_mapping(data, freq; m = 3). With the previous DNA example, we see a peak at 0.33. To get the corresponding mappings:
mappings = get_mappings(data, 0.33)
>> position of peak: 0.33 strengh of peak: 0.6
print(mappings)
>> ["A" : 0.54, "G" : 0.62, "T" : -0.57, "C" : 0.0]The function scans the vincinity of the provided goal frequency and returns the mapping for the found maxima. It also prints the positions and intensity of the peak so that you may control that you actually identified the desired peak and not a nearby sub-peak.
The codons A and G have a similar mapping, so they could potentially have similar functions : this is however not a necessity, as the spectral envelope only seeks to maximize the power-spectrum. If you want to study equivalency of categories, you should also check the results with a clustering algorithm like https://github.com/johncwok/IntegerIB.jl.git.
Finally, if you would like to transform your input time-series according to the mappings obtain with get_mappings, you can use the apply_mapping function as follow:
mapped_ts = apply_mapping(input_series, mapping)mapping being here the mapping returned by get_mappings.
If you used this module in a scientific publication, please consider citing the package it came from:
@article{nelias2021categoricaltimeseries,
title={CategoricalTimeSeries. jl: A toolbox for categorical time-series analysis},
author={Nelias, Corentin},
journal={Journal of Open Source Software},
volume={6},
number={67},
pages={3733},
year={2021}
}# installing the module
Using Pkg
Pkg.clone(“https://github.com/johncwok/SpectralEnvelope.jl.git”)
# importing the module
Using SpectralEnvelope- Implement windowing & averaging (periodogram bias correction).
- Implement bootstrap confidence intervals.