Man pages for autovarCore
Automated Vector Autoregression Models and Networks

apply_ln_transformationApplies the natural logarithm to the data set
assess_joint_sktestTests the skewness and kurtosis of a VAR model
assess_kurtosisTests the kurtosis of a VAR model
assess_portmanteauTests the white noise assumption for a VAR model using a...
assess_portmanteau_squaredTests the homeskedasticity assumption for a VAR model using a...
assess_skewnessTests the skewness of a VAR model
autovarReturn the best VAR models found for a time series data set
autovarCore-packageAutomated Vector Autoregression Models and Networks
coefficients_of_kurtosisKurtosis coefficients.
coefficients_of_skewnessSkewness coefficients.
competeReturns the winning model
day_dummiesCalculate weekday dummy variables
daypart_dummiesCalculate day-part dummy variables
explode_dummiesExplode dummies columns into separate dummy variables
impute_datamatrixImputes the missing values in the input data
invalid_maskCalculate a bit mask to identify invalid outlier dummies
model_is_stableEigenvalue stability condition checking
model_scoreReturn the model fit for the given varest model
needs_trendDetermines if a trend is required for the specified VAR model
outliers_columnDetermine the outliers column for the given column data
portmanteau_test_statisticsAn implementation of the portmanteau test.
print_correlation_matrixPrint the correlation matrix of the residuals of a model...
residual_outliersCalculate dummy variables to mask residual outliers
run_testsExecute a series of model validity assumptions
run_varCalculate the VAR model and apply restrictions
selected_columnsConvert an outlier_mask to a vector of column indices
select_valid_masksSelect and return valid dummy outlier masks
significance_from_pearson_coefCalculate the significance of a Pearson correlation...
sktest_joint_pSK test p-level
trend_columnsConstruct linear and quadratic trend columns
validate_paramsValidates the params given to the autovar function
validate_raw_dataframeValidates the dataframe given to the autovar function
autovarCore documentation built on May 2, 2019, 4:01 a.m.