A comparative study of HTM and other neural network models for online sequence learning with streaming data
Yuwei Cui, Chetan Surpur, Subutai Ahmad, Jeff Hawkins · 2016
Online sequence learning from streaming data is one of the most challenging topics in machine learning. Neural network models represent promising candidates for sequence learning due to their ability to learn and recognize complex temporal patterns. In this paper, we present a comparative study of Hierarchical Temporal Memory (HTM), a neurally-inspired model, and other feedforward and recurrent artificial neural network models on both artificial and real-world sequence prediction algorithms. HTM and long-short term memory (LSTM) give the best prediction accuracy. HTM additionally demonstrates many other features that are desirable for real-world sequence learning, such as fast adaptation to changes in the data stream, robustness to sensor noise and fault tolerance. These features make HTM an ideal candidate for online sequence learning problems.