HTM-MAT: An online prediction software toolbox based on cortical machine learning algorithm
This provides a software toolbox for researchers and practitioners interested in cortical machine learning algorithms, but it is incremental as it builds on existing HTM-based methods.
The paper tackles the problem of implementing cortical learning algorithms for online prediction by introducing HTM-MAT, a MATLAB toolbox, and shows that it is competitive and can outperform OS-ELM in sequential prediction tasks on benchmark and real-world datasets.
HTM-MAT is a MATLAB based toolbox for implementing cortical learning algorithms (CLA) including related cortical-like algorithms that possesses spatiotemporal properties. CLA is a suite of predictive machine learning algorithms developed by Numenta Inc. and is based on the hierarchical temporal memory (HTM). This paper presents an implementation of HTM-MAT with several illustrative examples including several toy datasets and compared with two sequence learning applications employing state-of-the-art algorithms - the recurrentjs based on the Long Short-Term Memory (LSTM) algorithm and OS-ELM which is based on an online sequential version of the Extreme Learning Machine. The performance of HTM-MAT using two historical benchmark datasets and one real world dataset is also compared with one of the existing sequence learning applications, the OS-ELM. The results indicate that HTM-MAT predictions are indeed competitive and can outperform OS-ELM in sequential prediction tasks.