Much of the worlds data is streaming, time-series data, where anomalies give significant information in critical situations. Yet detecting anomalies in streaming data is a difficult task, requiring detectors to process data in real-time, and learn while simultaneously making predictions. We present a novel anomaly detection technique based on an on-line sequence memory algorithm called Hierarchical Temporal Memory (HTM). We show results from a live application that detects anomalies in financial metrics in realtime. We also test the algorithm on NAB, a published benchmark for real-time anomaly detection, where our algorithm achieves best-in-class results.

Much of the world's data is streaming, time-series data, where anomalies give significant information in critical situations; examples abound in domains such as finance, IT, security, medical, and energy. Yet detecting anomalies in streaming data is a difficult task, requiring detectors to process data in real-time, not batches, and learn while simultaneously making predictions. There are no benchmarks to adequately test and score the efficacy of real-time anomaly detectors. Here we propose the Numenta Anomaly Benchmark (NAB), which attempts to provide a controlled and repeatable environment of open-source tools to test and measure anomaly detection algorithms on streaming data. The perfect detector would detect all anomalies as soon as possible, trigger no false alarms, work with real-world time-series data across a variety of domains, and automatically adapt to changing statistics. Rewarding these characteristics is formalized in NAB, using a scoring algorithm designed for streaming data. NAB evaluates detectors on a benchmark dataset with labeled, real-world time-series data. We present these components, and give results and analyses for several open source, commercially-used algorithms. The goal for NAB is to provide a standard, open source framework with which the research community can compare and evaluate different algorithms for detecting anomalies in streaming data.

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Six personality measures used in health psychology; the NEO Five-Factor Inventory (NEO-FFI) criterion measures of stress, self-reported health status, and coping; and a measure of social desirability were administered to samples of college students and adult community volunteers ( N = 589) in a series of four confirmatory and exploratory factor analytic studies. The hypothesis that the six independently developed personality measures of ego-strength, hardiness, self-esteem, self-efficacy, optimism, and maladjustment would share common variance and that a hierarchical factor model with a single, higher-order Health Proneness factor loading two lower-order factors—Self-Confidence and Adjustment—would account for the covariance in these measures was tested against single and three-factor models and confirmed. The factor model was examined with respect to general personality as represented in the “Big Five” Model. Adjustment was related negatively to NEO-FFI Neuroticism and positively to NEO-FFI Conscientiousness and Agreeableness, whereas Self-Confidence was related to NEO-FFI Extraversion. None of these relationships is extensive, nor does any one account for more than 40% of the variance. Evidence of the validity of Self-Confidence and Adjustment was found in their moderate relationships to measures of stress, health status, and coping, and in their weak relationships to social desirability and negative affectivity.

The original "Seven Motifs" set forth a roadmap of essential methods for the field of scientific computing, where a motif is an algorithmic method that captures a pattern of computation and data movement. 1 We present the Nine Motifs of Simulation Intelligence, a roadmap for the development and integration of the essential algorithms necessary for a merger of scientific computing, scientific simulation, and artificial intelligence. We call this merger simulation intelligence (SI), for short. We argue the motifs of simulation intelligence are interconnected and interdependent, much like the components within the layers of an operating system. Using this metaphor, we explore the nature of each layer of the simulation intelligence "operating system" stack (SI-stack) and the motifs therein:1. Multi-physics and multi-scale modeling 2. Surrogate modeling and emulation 3. Simulation-based inference 4. Causal modeling and inference 5. Agent-based modeling 6. Probabilistic programming 7. Differentiable programming 8. Open-ended optimization Machine programmingWe believe coordinated efforts between motifs offers immense opportunity to accelerate scientific discovery, from solving inverse problems in synthetic biology and climate science, to directing nuclear energy experiments and predicting emergent behavior in socioeconomic settings. We elaborate on each layer of the SI-stack, detailing the state-of-art methods, presenting examples to highlight challenges and opportunities, and advocating for specific ways to advance the motifs and the synergies from their combinations. Advancing and integrating these technologies can enable a robust and efficient hypothesis-simulation-analysis type of scientific method, which we introduce with several use-cases for human-machine teaming and automated science.