This library implements Hidden Markov Models (HMM) for time-inhomogeneous Markov processes. This means that, in contrast to many other HMM implementations, there can be different states and a different transition matrix at each time step.

Currently, this library provides an implementation of the Viterbi algorithm, which computes the most likely sequence of states. More HMM algorithms such as the forward backward algorithm will follow.

This library was initially created for HMM-based map matching according to the paper "NEWSON, Paul; KRUMM, John. Hidden Markov map matching through noise and sparseness. In: Proceedings of the 17th ACM SIGSPATIAL international conference on advances in geographic information systems. ACM, 2009. S. 336-343."

Graphhopper map matching is now using the hmm-lib for matching GPS positions to OpenStreetMap maps.

The offline-map-matching project demonstrates how to use the hmm-lib for map matching but does not provide integration to any particular map.

Besides map matching, the hmm-lib can also be used for other applications.

Except for testing, there are no dependencies to other libraries.

Contributions are welcome! For bug reports, please create an issue. For code contributions (e.g. new features or bugfixes), please create a pull request.

This library is licensed under the Apache 2.0 license.

• 0.2.0: Extend HmmProbabilities interface to include the observation
• 0.1.0: Initial release