All open source software developed and designed by the PreDiCT team can be found at https://github.com/predict-idlab. A list of selected open source software libraries developed and designed by our team is given below.
A Python toolkit which builds upon the Plotly graphing library to enable visualization of large time series. The visualizations are achieved by (1) separating the data between front- & back-end and (2) performing computational aggregation (downsampling) in te front-end. Take a look at our docs!
powershap is a feature selection method that uses statistical hypothesis testing and power calculations on Shapley values, enabling fast and intuitive wrapper-based feature selection.
RR-GCN is an extension of Relational Graph Convolutional Networks (R-GCN) in which the weights are randomly initialised and kept frozen (i.e. no training step is required). As such, our technique is unsupervised and the produced embeddings can be used for any downstream ML task/model. Surprisingly, empirical results indicate that the embeddings produced by our RR-GCN can be competitive to, and even sometimes outperform, end-to-end R-GCNs.
A Python library for pattern based time series analysis. This library started as an implementation of the Matrix Profile, and has since grown to bundles many Matrix Profile related publications. It is described in the paper A generalized matrix profile framework with support for contextual series analysis.