Communicates with EWSNet (https://ewsnet.github.io), a deep learning framework for modelling and anticipating regime shifts in dynamical systems, and returns the model's prediction for the inputted univariate time series.
Usage
ewsnet_predict(
x,
scaling = TRUE,
ensemble = 25,
envname,
weights_path = default_weights_path()
)Arguments
- x
A numeric vector of values to be tested.
- scaling
Boolean. If
TRUE, the time series will be scaled between 1 and 2 and scaled EWSNet model weights will be used. This is the recommended setting.- ensemble
A numeric value stating the number of models to average over. Options range from 1 to 25.
- envname
A string naming the Python environment prepared by
ewsnet_init().- weights_path
A string naming the path to model weights installed by
ewsnet_reset().
Value
A dataframe of EWSNet predictions. Values represent the estimated probability that the quoted event will occur.
Examples
#A dummy dataset of a hedgerow bird population
#monitored over 50 years.
abundance_data <- data.frame(time = seq(1:50),
abundance = rnorm(50,mean = 20))
#Activate python environment (only necessary
#on first opening of R session).
if (FALSE) { # \dontrun{
ewsnet_init(envname = "EWSNET_env")
} # }
#Generate EWSNet predictions.
if (FALSE) { # \dontrun{
pred <- ewsnet_predict(
abundance_data$abundance,
scaling = TRUE,
ensemble = 15,
envname = "EWSNET_env")
} # }