Drift Adaptable Models

In streaming data analysis, it is crucial to detect significant shifts in the data distribution or the accuracy of predictive models over time, a phenomenon known as concept drift. The package aims to identify when concept drift occurs and provide methodologies for adapting models in non-stationary environments. It offers a range of state-of-the-art techniques for detecting concept drift and maintaining model performance. Additionally, the package provides tools for adapting models in response to these changes, ensuring continuous and accurate predictions in dynamic contexts. Methods for concept drift detection are described in Tavares (2022) .

Heimdall

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.

Examples

Heimdall usage examples are organized according to drift detection:

• Examples

Installation

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")

Bugs and Feature Requests

If you encounter any issues or would like to suggest new features, please open an issue:

• GitHub Issues