  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.
