Hierarchical Temporal Memory (HTM) is an unsupervised learning algorithm
inspired by the features of the neocortex that can be used to continuously
process stream data and detect anomalies, without requiring a large amount of
data for training nor requiring labeled data. HTM is also able to continuously
learn from samples, providing a model that is always up-to-date with respect to
observations. These characteristics make HTM particularly suitable for
supporting online failure prediction in cloud systems, which are systems with a
dynamically changing behavior that must be monitored to anticipate problems.
This paper presents the first systematic study that assesses HTM in the context
of failure prediction. The results that we obtained considering 72
configurations of HTM applied to 12 different types of faults introduced in the
Clearwater cloud system show that HTM can help to predict failures with
sufficient effectiveness (F-measure = 0.76), representing an interesting
practical alternative to (semi-)supervised algorithms.

该研究介绍了 HTM 在云系统在线故障预测方面应用的可行性，其结果证明 HTM 可以高效地帮助预测故障，是（半）监督算法的有趣实际替代品。

基于分层时间记忆的云故障预测：实证评估

Cloud Failure Prediction with Hierarchical Temporal Memory: An Empirical  Assessment

Hierarchical Temporal Memory (HTM) is a biologically inspired machine
intelligence technology that mimics the architecture and processes of the
neocortex. In this white paper we describe how to encode data as Sparse
Distributed Representations (SDRs) for use in HTM systems. We explain several
existing encoders, which are available through the open source project called
NuPIC, and we discuss requirements for creating encoders for new types of data.

本篇论文描述了如何将数据编码为少量分布式表示（SDRs），并将其用于 HTM 系统，这是一种生物学启发的机器智能技术，模拟了新皮层的结构和过程。

HTM 系统的数据编码

Encoding Data for HTM Systems

The ability to recognize and predict temporal sequences of sensory inputs is
vital for survival in natural environments. Based on many known properties of
cortical neurons, hierarchical temporal memory (HTM) sequence memory is
recently proposed as a theoretical framework for sequence learning in the
cortex. In this paper, we analyze properties of HTM sequence memory and apply
it to sequence learning and prediction problems with streaming data. We show
the model is able to continuously learn a large number of variable-order
temporal sequences using an unsupervised Hebbian-like learning rule. The sparse
temporal codes formed by the model can robustly handle branching temporal
sequences by maintaining multiple predictions until there is sufficient
disambiguating evidence. We compare the HTM sequence memory with other sequence
learning algorithms, including statistical methods: autoregressive integrated
moving average (ARIMA), feedforward neural networks: online sequential extreme
learning machine (ELM), and recurrent neural networks: long short-term memory
(LSTM) and echo-state networks (ESN), on sequence prediction problems with both
artificial and real-world data. The HTM model achieves comparable accuracy to
other state-of-the-art algorithms. The model also exhibits properties that are
critical for sequence learning, including continuous online learning, the
ability to handle multiple predictions and branching sequences with high order
statistics, robustness to sensor noise and fault tolerance, and good
performance without task-specific hyper- parameters tuning. Therefore the HTM
sequence memory not only advances our understanding of how the brain may solve
the sequence learning problem, but is also applicable to a wide range of
real-world problems such as discrete and continuous sequence prediction,
anomaly detection, and sequence classification.

本文提出了分层时序记忆（HTM）的序列记忆，使用无监督的 Hebbian-like 学习规则可以连续学习大量变序列。 HTM 序列记忆能够处理多元序列及预测，还具有连续的在线学习、处理多元及多分支序列的能力、传感器噪声稳健性和容错性等优点，可应用于离散和连续序列预测、异常检测和序列分类等多领域。