autovarCore: Automated Vector Autoregression Models and Networks

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Automatically find the best vector autoregression models and networks for a given time series data set. 'AutovarCore' evaluates eight kinds of models: models with and without log transforming the data, lag 1 and lag 2 models, and models with and without day dummy variables. For each of these 8 model configurations, 'AutovarCore' evaluates all possible combinations for including outlier dummies (at 2.5x the standard deviation of the residuals) and retains the best model. Model evaluation includes the Eigenvalue stability test and a configurable set of residual tests. These eight models are further reduced to four models because 'AutovarCore' determines whether adding day dummies improves the model fit.

Author
Ando Emerencia [aut, cre]
Date of publication
2015-07-01 14:41:42
Maintainer
Ando Emerencia <ando.emerencia@gmail.com>
License
MIT + file LICENSE
Version
1.0-0

View on CRAN

Man pages

apply_ln_transformation
Applies the natural logarithm to the data set
assess_joint_sktest
Tests the skewness and kurtosis of a VAR model
assess_kurtosis
Tests the kurtosis of a VAR model
assess_portmanteau
Tests the white noise assumption for a VAR model using a...
assess_portmanteau_squared
Tests the homeskedasticity assumption for a VAR model using a...
assess_skewness
Tests the skewness of a VAR model
autovar
Return the best VAR models found for a time series data set
autovarCore-package
Automated Vector Autoregression Networks
compete
Returns the winning model
day_dummies
Calculate day dummy variables
daypart_dummies
Calculate day-part dummy variables
explode_dummies
Explode dummies columns into separate dummy variables
impute_datamatrix
Imputes the missing values in the input data
invalid_mask
Calculate a bit mask to identify invalid outlier dummies
model_is_stable
Eigenvalue stability condition checking
model_score
Return the model fit for the given varest model
needs_trend
Determines if a trend is required for the specified VAR model
rcpp_hello_world
Simple function using Rcpp
residual_outliers
Calculate dummy variables to mask residual outliers
run_tests
Execute a series of model validity assumptions
run_var
Calculate the VAR model and apply restrictions
selected_columns
Convert an outlier_mask to a vector of column indices
select_valid_masks
Select and return valid dummy outlier masks
trend_columns
Construct linear and quadratic trend columns
validate_params
Validates the params given to the autovar function
validate_raw_dataframe
Validates the dataframe given to the autovar function

Files in this package

autovarCore
autovarCore/inst
autovarCore/inst/bash
autovarCore/inst/bash/install-package-dependencies.sh
autovarCore/inst/help_files
autovarCore/inst/help_files/index.html
autovarCore/inst/help_files/assess_portmanteau_squared.html
autovarCore/inst/help_files/needs_trend.html
autovarCore/inst/help_files/residual_outliers.html
autovarCore/inst/help_files/select_valid_masks.html
autovarCore/inst/help_files/autovarCore-package.html
autovarCore/inst/help_files/img
autovarCore/inst/help_files/img/glyphicons-halflings-white.png
autovarCore/inst/help_files/img/glyphicons-halflings.png
autovarCore/inst/help_files/daypart_dummies.html
autovarCore/inst/help_files/explode_dummies.html
autovarCore/inst/help_files/impute_datamatrix.html
autovarCore/inst/help_files/run_tests.html
autovarCore/inst/help_files/model_is_stable.html
autovarCore/inst/help_files/assess_joint_sktest.html
autovarCore/inst/help_files/validate_params.html
autovarCore/inst/help_files/css
autovarCore/inst/help_files/css/bootstrap.css
autovarCore/inst/help_files/css/bootstrap.min.css
autovarCore/inst/help_files/css/staticdocs.css
autovarCore/inst/help_files/css/bootstrap-responsive.css
autovarCore/inst/help_files/css/bootstrap-responsive.min.css
autovarCore/inst/help_files/css/highlight.css
autovarCore/inst/help_files/validate_raw_dataframe.html
autovarCore/inst/help_files/assess_kurtosis.html
autovarCore/inst/help_files/trend_columns.html
autovarCore/inst/help_files/run_var.html
autovarCore/inst/help_files/rcpp_hello_world.html
autovarCore/inst/help_files/assess_portmanteau.html
autovarCore/inst/help_files/autovar.html
autovarCore/inst/help_files/invalid_mask.html
autovarCore/inst/help_files/compete.html
autovarCore/inst/help_files/assess_skewness.html
autovarCore/inst/help_files/js
autovarCore/inst/help_files/js/bootstrap.js
autovarCore/inst/help_files/js/bootstrap.min.js
autovarCore/inst/help_files/apply_ln_transformation.html
autovarCore/inst/help_files/day_dummies.html
autovarCore/inst/help_files/selected_columns.html
autovarCore/inst/help_files/model_score.html
autovarCore/tests
autovarCore/tests/testthat.R
autovarCore/tests/testthat
autovarCore/tests/testthat/test_model_score.r
autovarCore/tests/testthat/test_model_is_stable.r
autovarCore/tests/testthat/test_needs_trend.r
autovarCore/tests/testthat/test_apply_ln_transformation.r
autovarCore/tests/testthat/test_autovar.r
autovarCore/tests/testthat/test_assess_kurtosis.r
autovarCore/tests/testthat/test_validate_raw_dataframe.r
autovarCore/tests/testthat/test_assertions.r
autovarCore/tests/testthat/test_explode_dummies.r
autovarCore/tests/testthat/test_impute_datamatrix.r
autovarCore/tests/testthat/test_daypart_dummies.r
autovarCore/tests/testthat/test_config.r
autovarCore/tests/testthat/test_assess_skewness.r
autovarCore/tests/testthat/test_validate_params.r
autovarCore/tests/testthat/test_assess_portmanteau_squared.r
autovarCore/tests/testthat/test_run_var.r
autovarCore/tests/testthat/test_assess_portmanteau.r
autovarCore/tests/testthat/test_select_valid_masks.r
autovarCore/tests/testthat/test_selected_columns.r
autovarCore/tests/testthat/test_residual_outliers.r
autovarCore/tests/testthat/test_assess_joint_sktest.r
autovarCore/tests/testthat/test_day_dummies.r
autovarCore/tests/testthat/test_compete.r
autovarCore/tests/testthat/test_run_tests.r
autovarCore/tests/testthat/test_trend_columns.r
autovarCore/tests/testthat/test_invalid_mask.r
autovarCore/src
autovarCore/src/rcpp_hello_world.cpp
autovarCore/src/RcppExports.cpp
autovarCore/NAMESPACE
autovarCore/R
autovarCore/R/autovar.r
autovarCore/R/daypart_dummies.r
autovarCore/R/on_unload.r
autovarCore/R/trend_columns.r
autovarCore/R/apply_ln_transformation.r
autovarCore/R/model_is_stable.r
autovarCore/R/selected_columns.r
autovarCore/R/assess_portmanteau_squared.r
autovarCore/R/run_tests.r
autovarCore/R/assess_kurtosis.r
autovarCore/R/validate_raw_dataframe.r
autovarCore/R/invalid_mask.r
autovarCore/R/assess_portmanteau.r
autovarCore/R/explode_dummies.r
autovarCore/R/select_valid_masks.r
autovarCore/R/compete.r
autovarCore/R/RcppExports.R
autovarCore/R/assess_joint_sktest.r
autovarCore/R/model_score.r
autovarCore/R/run_var.r
autovarCore/R/impute_datamatrix.r
autovarCore/R/assess_skewness.r
autovarCore/R/needs_trend.r
autovarCore/R/assertions.r
autovarCore/R/config.r
autovarCore/R/day_dummies.r
autovarCore/R/validate_params.r
autovarCore/R/residual_outliers.r
autovarCore/README.md
autovarCore/MD5
autovarCore/DESCRIPTION
autovarCore/man
autovarCore/man/residual_outliers.Rd
autovarCore/man/model_score.Rd
autovarCore/man/trend_columns.Rd
autovarCore/man/run_var.Rd
autovarCore/man/model_is_stable.Rd
autovarCore/man/select_valid_masks.Rd
autovarCore/man/validate_raw_dataframe.Rd
autovarCore/man/compete.Rd
autovarCore/man/impute_datamatrix.Rd
autovarCore/man/run_tests.Rd
autovarCore/man/assess_portmanteau.Rd
autovarCore/man/invalid_mask.Rd
autovarCore/man/selected_columns.Rd
autovarCore/man/apply_ln_transformation.Rd
autovarCore/man/assess_portmanteau_squared.Rd
autovarCore/man/assess_kurtosis.Rd
autovarCore/man/explode_dummies.Rd
autovarCore/man/needs_trend.Rd
autovarCore/man/autovar.Rd
autovarCore/man/validate_params.Rd
autovarCore/man/assess_joint_sktest.Rd
autovarCore/man/assess_skewness.Rd
autovarCore/man/daypart_dummies.Rd
autovarCore/man/day_dummies.Rd
autovarCore/man/autovarCore-package.Rd
autovarCore/man/rcpp_hello_world.Rd
autovarCore/LICENSE