Modeling Dependencies in Multiple Parallel Data Streams with Hyperdimensional Computing

This work presents an approach for modeling statistical dependencies in multivariate discrete sequences by using hyperdimensional random vectors. The system takes any number of parallel sequences as inputs and learns to predict the future states of these streams using the mutual dependencies between the inputs. Performance of the system is tested in an activity recognition task with data from multiple worn sensors. The results show that the approach outperforms the existing baseline results in the task and demonstrate that the system is capable to account for the varying reliability of different input streams.