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.
AutovarCore finds the best fitting VAR models for a given time series data set that pass the selected set of residual assumptions. AutovarCore will also generate Granger causality networks given a data frame (this functionality is not yet implemented). AutovarCore is a simplified/efficient version of Autovar.
To install, type the following:
You should use Autovar if you
You should use AutovarCore if you
library('autovarCore') # 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.) library('foreign') # This example data set can be downloaded from # https://autovar.nl/datasets/aug_pp5_da.sav suppressWarnings(dfile <- 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 print(models_found[])