Heimdall is a tool designed for concept drift detection in continuous data streams. When analyzing time series data, it is possible to observe significant changes in the data distribution or in the accuracy of predictive models during the forecasting process. These changes are known as concept drift.
The goal of the Heimdall package is to detect when concept drift occurs, enabling the detection and adaptation of models in non-stationary contexts. The package offers several state-of-the-art methods to identify and handle concept drift, as well as techniques for efficient model adaptation.
Heimdall is particularly useful when you need to move quickly from the theory of concept drift to a working streaming implementation. The package includes detectors for both real concept drift and virtual concept drift, and the repository examples are designed to help users understand that distinction through small, reproducible workflows.
Heimdall usage examples are organized to help users learn the package progressively, not only to list detector names.
The examples directory contains:
- rendered walkthroughs for each detector;
- extracted R scripts for quick reuse;
- figure outputs and document exports;
- a dedicated guide describing the purpose of each example.
Recommended starting points:
dfr_adwinfor a simple univariate distribution-monitoring example;dfr_ddmfor an error-based supervised example;dfr_aeddfor a multivariate unsupervised example.
Browse the materials here:
The latest version of Heimdall is available on CRAN:
install.packages("heimdall")You can install the development version directly from GitHub:
# install.packages("devtools")
library(devtools)
devtools::install_github("cefet-rj-dal/heimdall", force = TRUE, upgrade = "never")If you encounter any issues or would like to suggest new features, please open an issue: