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 weekday 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 weekday dummies improves the model fit.


The DESCRIPTION file: This package was not yet installed at build time.
Index: This package was not yet installed at build time.
Please see the help of the autovar function for information on how to use this package.


Emerencia, A. C., L. van der Krieke, E. H. Bos, P. de Jonge, N. Petkov and M. Aiello (2016), Automating Vector Autoregression on Electronic Patient Diary Data, IEEE Journal of Biomedical and Health Informatics, 20(2): 631-643,

See also


# AutovarCore requires input data in data.frame format.
# If you have data in a .csv, .dta, or .sav file, use
# the 'foreign' library to load this data into R first.
# (You may need to type:
#    install.packages('foreign')
#  if you do not have the foreign library installed on
#  your system.)

# This example data set can be downloaded from
suppressWarnings(dfile <- foreign::read.spss('~/Downloads/aug_pp5_da.sav'))
dframe <- data.frame(Activity = dfile$Activity, Depression = dfile$Depression)

# Call autovar with the given data frame. Type:
#   ?autovar
# (after having typed "library('autovarCore')") to see
# which other options are available.
models_found <- autovar(dframe, selected_column_names = c('Activity', 'Depression'))

# Show details for the best model found
# }