mvgam
{mvgam} R 📦 to fit Dynamic Bayesian Generalized Additive Models for multivariate modeling and forecasting
mm4benchmark
R package implementing the multivariate (multi-univariate) extension of the benchmarks used for the M
https://github.com/eonu/sequentia
Scikit-Learn compatible HMM and DTW based sequence machine learning algorithms in Python.
https://github.com/cerlymarco/tspiral
A python package for time series forecasting with scikit-learn estimators.
composits
https://github.com/dynamicsandneuralsystems/pyspi
Comparative analysis of pairwise interactions in multivariate time series.
https://github.com/filippomb/time-series-cluster-kernel
Kernel similarity for classification and clustering of multi-variate time series with missing values.
https://github.com/boniolp/dcam
[SIGMOD 2022] Python code for "Dimension-wise Class Activation Map for Multivariate Time Series Classification"
cleaned-swansf-dataset
The SWAN-SF dataset is now fully preprocessed, optimized, and ready for binary classification tasks. Our team is excited to release the enhanced version of the SWAN-SF dataset across all five partitions.
swansf-datapreprocessing-sampling-notebooks
These notebooks provide a comprehensive workflow, from start to finish, for processing and analyzing the SWAN-SF dataset. They include detailed steps for reading the dataset files, performing full preprocessing, and executing classification.
extendeddfml
R package implementing the Extended Dynamic Factor Machine Learner multivariate forecasting method
clustervar
Estimates latent class vector-autoregressive models via EM algorithm on time-series data for model-based clustering and classification. Includes model selection criteria for selecting the number of lags and clusters.