UnrollingAverages

A Julia package to deconvolve ("unroll") moving averages of time series to get the original ones back.

https://github.com/inphyt/unrollingaverages.jl

Science Score: 28.0%

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Keywords

julia julia-language julia-package reverse-engineering statistics stats time-series time-series-analysis time-series-data time-series-database time-series-transformation

Keywords from Contributors

raytracer julialang

Last synced: 6 months ago · JSON representation ·

Repository

A Julia package to deconvolve ("unroll") moving averages of time series to get the original ones back.

Basic Info

Host: GitHub
Owner: InPhyT
License: mit
Language: Julia
Default Branch: main
Homepage: https://inphyt.github.io/UnrollingAverages.jl/dev
Size: 752 KB

Statistics

Stars: 19
Watchers: 0
Forks: 2
Open Issues: 0
Releases: 0

Topics

julia julia-language julia-package reverse-engineering statistics stats time-series time-series-analysis time-series-data time-series-database time-series-transformation

Created about 4 years ago · Last pushed about 3 years ago

Metadata Files

Readme Funding License Citation

README.md

UnrollingAverages.jl

UnrollingAverages is a Julia package aimed at deconvolving (or unrolling) moving averages of time series to get the original ones back.

UnrollingAverages currently assumes that the moving average is a simple moving average. Further relaxations and extensions may come in the future, see Future Developments section.

Installation

Press ] in the Julia REPL and then

nothing pkg> add UnrollingAverages

Usage

The package exports a single function called unroll: it returns a Vector whose elements are the possible original time series.

julia unroll( moving_average::Vector{Float64}, window::Int64; initial_conditions::U=nothing, assert_natural::Bool=false ) where {U<:Union{Tuple{Vararg{Union{Int64,Float64}}},Nothing}}

Arguments

moving_average: the time series representing the moving average to unroll ;
window: the width of the moving average ;
initial_conditions: the initial values of the original time series to be recovered. It may be a Tuple of window-1 positive integer values, or nothing if initial conditions are unknown. Currently it is not possible to specify values in the middle of the time series, this may be a feature to be added in the future ;
assert_natural default boolean argument. If true, the pipeline will try to recover a time series of natural numbers only. More then one acceptable time series (where "acceptable" means that it reproduces moving_average) may be found and all will be returned.

A few remarks:

If isnothing(initial_conditions):
- if assert_natural, then an internal unroll_iterative method is called, which tries to exactly recover the whole time series, initial conditions included. Enter ?UnrollingAverages.unroll_iterative in a Julia to read further details;
- if !assert_natural, then an internal unroll_linear_approximation method is called. See this StackExchange post. NB: this is an approximated method, it will generally not return the exact original time series;
If typeof(initial_conditions) <: Ntuple{window-1, <:Union{Int64,Float64}}, then an internal unroll_recursive method is called, which exactly recovers the time series. Mathematical details about this function are reported in the documentation, and you may read more by entering ?UnrollingAverages.unroll_recursive.

Future Developments

Modify initial_conditions argument of unroll so that it accepts known values throughout the series;
Implement reversing methods for other types of moving averages .

How to Contribute

If you wish to change or add some functionality, please file an issue. Some suggestions may be found in the Future Developments section.

How to Cite

If you use this package in your work, please cite this repository using the metadata in CITATION.bib.

Announcements

Owner

Name: Interdisciplinary Physics Team (InPhyT)
Login: InPhyT
Kind: organization
Email: inphyt@gmail.com
Location: Turin, Italy

Website: https://inphyt.github.io
Twitter: In_Phy_T
Repositories: 17
Profile: https://github.com/InPhyT

Complex Systems Modelling Group: Computational Social Science, Epidemiology and Neuroscience.

Citation (CITATION.bib)

@software{Monticone_Moroni_UnrollingAverages_2021,
         abstract     = {A Julia package to deconvolve ("unroll") moving averages of time series to get the original ones back.},
         author       = {Monticone, Pietro and Moroni, Claudio},
         doi          = {10.5281/zenodo.5725301},
         institution  = {University of Turin},
         keywords     = {Julia Language, Time Series, Statistics, Data Science, Data Analysis, Reverse Engineering},
         license      = {MIT},
         organization = {Interdisciplinary Physics Team (InPhyT)},
         title        = {UnrollingAverages.jl},
         url          = {https://doi.org/10.5281/zenodo.5725301},
         year         = {2021}
         }

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Avg Commits per committer: 14.25
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Total pull requests: 6
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Average time to close pull requests: 16 days
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Total pull request authors: 3
Average comments per issue: 4.0
Average comments per pull request: 0.17
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InterdisciplinaryPhysicsTeam (3)
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Total packages: 1
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Total dependent packages: 0
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Total versions: 4

juliahub.com: UnrollingAverages

A Julia package to deconvolve ("unroll") moving averages of time series to get the original ones back.

Homepage: https://inphyt.github.io/UnrollingAverages.jl/dev
Documentation: https://docs.juliahub.com/General/UnrollingAverages/stable/
License: MIT
Latest release: 0.2.3
published about 3 years ago

Versions: 4
Dependent Packages: 0
Dependent Repositories: 0

Rankings

Dependent repos count: 9.9%

Stargazers count: 24.1%

Average: 26.6%

Forks count: 33.3%

Dependent packages count: 38.9%

Last synced: 6 months ago