BigVAR
Dimension Reduction Methods for Multivariate Time Series
Science Score: 33.0%
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○CITATION.cff file
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✓codemeta.json file
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○.zenodo.json file
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○DOI references
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✓Academic publication links
Links to: arxiv.org -
✓Committers with academic emails
1 of 5 committers (20.0%) from academic institutions -
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○Scientific vocabulary similarity
Low similarity (12.5%) to scientific vocabulary
Repository
Dimension Reduction Methods for Multivariate Time Series
Basic Info
- Host: GitHub
- Owner: wbnicholson
- Language: R
- Default Branch: master
- Size: 11.4 MB
Statistics
- Stars: 61
- Watchers: 10
- Forks: 17
- Open Issues: 22
- Releases: 0
Metadata Files
README.md
BigVAR
Tools for modeling sparse high-dimensional multivariate time series
R Package
For a demonstration of the package's capabilities, see the recently updated BigVAR Tutorial, the Shiny App, or the slightly out of date user guide available on Arxiv.
Note: This package utilizes C++11, so it requires a compiler with C++11 support (which should include most modern compilers) and a later version of R (version 3.1 is the oldest that I can confirm works).
To install the development version of BigVAR, after installing the devtools package, run the following commands
```R library(devtools)
install_github("wbnicholson/BigVAR/BigVAR") ```
The stable version is available on cran.
If you experience any bugs or have feature requests, contact me at wbn8@cornell.edu.
Python Package
A minimalist Python implementation (partially inspired by this abandoned effort) has been released. Currently, it only has the capability to fit Basic or Elastic Net penalty structures. Feel free to suggest other functionality or submit pull requests.
Installation
In order to install the Python implementation, clone the repository, navigate to the python directory and run
bash
pip install -e .
Usage
An example script is below
```python
import numpy as np from BigVAR.BigVARSupportFunctions import MultVARSim, CreateCoefMat from BigVAR.BigVARClass import BigVAR,rolling_validate
example coefficient matrix
k=3;p=4 B1=np.array([[.4,-.02,.01],[-.02,.3,.02],[.01,.04,0.3]]) B2=np.array([[.2,0,0],[0,.3,0],[0,0,0.13]]) B=np.concatenate((B1,B2),axis=1) B=np.concatenate((B,np.zeros((k,2k))),axis=1) A=CreateCoefMat(B,p,k) Y=MultVARSim(A,p,k,0.01np.identity(3),500) VARX={}
construct BigVAR object:
Arguments:
Y T x k multivariate time series
p: lag order
penalty structure (only Basic and BasicEN supported)
granularity (depth of grid and number of gridpoints)
T1: Start of rolling validation
T2: End of rolling validation
alpha: elastic net alpha candidate
VARX: VARX specifications as dict with keys k (number of endogenous series), s (lag order of exogenous series)
mod=BigVAR(Y,p,"Basic",[50,10],50,80,alpha=0.4,VARX=VARX)
res=rolling_validate(mod)
coefficient matrix
res.B
out of sample MSFE
res.oos_msfe
optimal lambda
res.opt_lambda ```
Owner
- Name: Will Nicholson
- Login: wbnicholson
- Kind: user
- Location: New York, NY
- Website: http://www.wbnicholson.com
- Repositories: 3
- Profile: https://github.com/wbnicholson
GitHub Events
Total
- Issues event: 1
- Watch event: 3
- Push event: 3
Last Year
- Issues event: 1
- Watch event: 3
- Push event: 3
Committers
Last synced: over 2 years ago
Top Committers
| Name | Commits | |
|---|---|---|
| Will Nicholson | w****n@g****m | 82 |
| Will Nicholson | w****8@c****u | 25 |
| jonlachmann | j****n@l****u | 7 |
| Will Nicholson | w****l@l****n | 1 |
| Yixuan Qiu | y****u@c****e | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 10 months ago
All Time
- Total issues: 38
- Total pull requests: 11
- Average time to close issues: 2 months
- Average time to close pull requests: 7 days
- Total issue authors: 22
- Total pull request authors: 3
- Average comments per issue: 2.61
- Average comments per pull request: 0.55
- Merged pull requests: 10
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 1
- Pull requests: 2
- Average time to close issues: N/A
- Average time to close pull requests: 3 days
- Issue authors: 1
- Pull request authors: 1
- Average comments per issue: 0.0
- Average comments per pull request: 0.5
- Merged pull requests: 1
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- FransAndersen (6)
- jonlachmann (5)
- extrospective (4)
- sehoff (2)
- weiweilars (2)
- TuSKan (2)
- alexsuarez94 (2)
- msramirezgo (1)
- wysjdy0511 (1)
- michael-aksonov (1)
- bdemeshev (1)
- TigerZhao007 (1)
- dk1453 (1)
- runnytone (1)
- Blaieet (1)
Pull Request Authors
- jonlachmann (11)
- yixuan (1)
- wbnicholson (1)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
-
Total downloads:
- cran 459 last-month
- Total docker downloads: 1,419
- Total dependent packages: 2
- Total dependent repositories: 2
- Total versions: 10
- Total maintainers: 1
cran.r-project.org: BigVAR
Dimension Reduction Methods for Multivariate Time Series
- Homepage: https://github.com/wbnicholson/BigVAR
- Documentation: http://cran.r-project.org/web/packages/BigVAR/BigVAR.pdf
- License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
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Latest release: 1.1.3
published about 1 year ago
Rankings
Maintainers (1)
Dependencies
- R >= 3.5.0 depends
- lattice * depends
- methods * depends
- MASS * imports
- Rcpp * imports
- abind * imports
- grDevices * imports
- graphics * imports
- stats * imports
- utils * imports
- zoo * imports
- MCS * suggests
- codetools * suggests
- expm * suggests
- gridExtra * suggests
- knitr * suggests
- quantmod * suggests
- rmarkdown * suggests
- numba *
- numpy *
- statsmodels *