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Predictive Analytics with Numenta Machine Intelligence

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Presentation given by Alex Lavin, Research and Software Engineer at Numenta - SF Data Science Meetup August 2, 2016.

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Predictive Analytics with Numenta Machine Intelligence

  1. 1. PREDICTIVE ANALYTICS WITH NUMENTAMACHINE INTELLIGENCE SF Data Science Meetup August 2, 2016 Alex Lavin alavin@numenta.com @theAlexLavin
  2. 2. OUTLINE 1. Online, streaming analytics 2. Intro to Hierarchical Temporal Memory (HTM) 3. Applying HTM 1. Real-time anomaly detection 2. Real-time prediction 3. Open-source engine and streams 4. Wrap up 1. Summary 2. Q&A
  3. 3. BATCH STREAMING DATA
  4. 4. THE STREAMING ANALYTICS PROBLEM Given all past input and current input, compute the state of the system right now. Must report decision, perform any retraining, bookkeeping, etc. before next input arrives. • No look-ahead – online, not batch • No training/test set split • System must be automated, and customized to each stream • Unsupervised, continuous learning
  5. 5. REAL-TIME ANALYTICS • Enormous increase in the availability of streaming, time-series data • Prediction is fundamental to real-time analytics, and valuable in all domains! Monitoring IT infrastructure Financials data Tracking vehicles Real-time health monitoring Energy consumption
  6. 6. OUTLINE 1. Online, streaming analytics 2. Intro to Hierarchical Temporal Memory (HTM) 3. Applying HTM 1. Real-time anomaly detection 2. Real-time prediction 3. Open-source engine and streams 4. Wrap up 1. Summary 2. Q&A
  7. 7. RESEARCH @ NUMENTA Neuroscience Theories Computational Frameworks Machine Intelligence Neurobiology Data
  8. 8. HIERARCHICAL TEMPORAL MEMORY (HTM) HTM is a powerful sequence memory derived from recent findings in experimental neuroscience. • High capacity memory-based system • Models complex, high-order temporal sequences • Inherently streaming • Continuously learning and predicting • No need to tune hyper-parameters • Robust and fault-tolerant • Runs in real time on a laptop • Open source: github.com/numenta
  9. 9. HIERARCHICAL TEMPORAL MEMORY (HTM) Want to dive in to HTM? • http://numenta.com/learn • BaMI • Research papers • HTM School • http://numenta.org for NuPIC • https://discourse.numenta.org • Social media:
  10. 10. OUTLINE 1. Online, streaming analytics 2. Intro to Hierarchical Temporal Memory (HTM) 3. Applying HTM 1. Real-time anomaly detection 2. Real-time prediction 3. Open-source engine and streams 4. Wrap up 1. Summary 2. Q&A
  11. 11. HTM PREDICTS FUTURE INPUT Active Inactive Depolarized (predicted) HTM 𝑎(𝑥$) 𝜋(𝑥$) 𝑥$ • Input to the system is a stream of data: • Encoded into a sparse, high dimensional vector • Learns temporal sequences in inputstream: • Makes a prediction in the form of a sparse vector: • 𝜋(𝑥$) represents a predictionfor upcoming input: 𝑥$ 𝑎(𝑥$) 𝜋(𝑥$) 𝑎(𝑥$'()
  12. 12. HTM Raw anomaly score Anomaly likelihood • 𝑠$ is an instantaneous measure of prediction error • 0 if input was perfectly prediction • 1 if it was completely unpredicted • Could threshold it directly to report anomalies, but in very noisy environments we can do better 𝑥$ 𝑎(𝑥$) 𝜋(𝑥$) 𝐿$ 𝑠$ ANOMALY DETECTION WITH HTM
  13. 13. ANOMALY LIKELIHOOD Second order measure: did the predictability of the metric change? 1. Estimate historical distribution of raw anomaly scores 2. Check if recent scores are very different
  14. 14. ANOMALY LIKELIHOOD Second order measure: did the predictability of the metric change? 1. Estimate historical distribution of raw anomaly scores 2. Check if recent scores are very different
  15. 15. ANOMALY BENCHMARK Detector Standard Profile Reward Low FP Reward Low FN Perfect 100 100 100 Numenta HTM 65.3 58.6 69.4 Multinomial Relative Entropy 54.6 47.6 58.8 Twitter ADVec v1.0.0 47.1 33.6 53.5 Etsy Skyline 35.7 27.1 44.5 Sliding Threshold 30.7 12.1 38.3 Bayesian Online Changepoint 17.7 3.2 32.2 EXPoSE 16.4 3.2 26.9 Random 11 1.2 19.5 Null 0 0 0 https://github.com/numenta/NAB
  16. 16. MULTIPLE STREAMS Ahmad & Purdy, "Real-Time Anomaly Detection for StreamingAnalytics": https://arxiv.org/abs/1607.02480
  17. 17. PREDICTION USES SOFTMAX CLASSIFIER HTM SDR Classifier • Classifier maps 𝑎(𝑥$) to a probability distribution over inputs using a linear classifier plus softmax • Classifier trained to optimize negative log likelihood • System can predict multiple time steps into the future • Weights are updated continuously • Can predict categories and scalar values 𝑎(𝑥$)𝑥$ 𝑃(𝑥$',|𝑥$)
  18. 18. 2015-04-20 Monday 2015-04-21 Tuesday 2015-04-22 Wednesday 2015-04-23 Thursday 2015-04-24 Friday 2015-04-25 Saturday 2015-04-26 Sunday 0 k 5 k 10 k 15 k 20 k 25 k 30 k PassengerCountin30minwindow A B C 0.6 0.8 1.0 SE 0.20 0.25 0.30 0.35 E 1.5 2.0 2.5 g-likelihood D NYC Taxi demand Source: http://www.nyc.gov/html/tlc/html/about/trip_record_data.shtml ? PERFORMANCE ON REAL-WORLD STREAMING DATA SOURCES
  19. 19. Cui et al, "Continuous online sequence learning with an unsupervised neural network model": https://arxiv.org/abs/1512.05463 PERFORMANCE ON REAL-WORLD STREAMING DATA SOURCES
  20. 20. New pattern introduced: 20% increase of night taxi demand 20% decrease of morning taxi demand Cui et al, "Continuous online sequence learning with an unsupervised neural network model": https://arxiv.org/abs/1512.05463 FAST ADAPTATION TO CHANGES IN THE DATA STREAMS
  21. 21. OUTLINE 1. Online, streaming analytics 2. Intro to Hierarchical Temporal Memory (HTM) 3. Applying HTM 1. Real-time anomaly detection 2. Real-time prediction 3. Open-source engine and streams 4. Wrap up 1. Summary 2. Q&A
  22. 22. HTM ENGINE FOR STREAMING ANALYTICS Datacenter server anomalies Rogue human behavior Geospatial tracking Stock anomalies Social media streams (Twitter) HTM High Order Sequence Memory Encoder SDRData Prediction Anomaly detection Classification
  23. 23. HTM ENGINE + RIVER VIEW HTM Engine code: https://github.com/numenta/numenta-apps River View service: http://data.numenta.org/
  24. 24. OUTLINE 1. Online, streaming analytics 2. Intro to Hierarchical Temporal Memory (HTM) 3. Applying HTM 1. Real-time anomaly detection 2. Real-time prediction 3. Open-source engine and streams 4. Wrap up 1. Summary 2. Q&A
  25. 25. TAKE HOME POINTS Streaming data is the future HTM is powerful for predictive analytics Open source!
  26. 26. THANK YOU! • Collaborators: • Jeff Hawkins • Subutai Ahmad • Yuwei Cui • Scott Purdy • Contact: • alavin@numenta.com • @theAlexLavin
  27. 27. RESOURCES • Open Source Repositories: • Algorithm code: github.com/numenta/nupic • Applications: github.com/numenta/numenta-apps • NAB code + paper: github.com/numenta/nab • Apache Flink: github.com/htm-community/flink-htm • Learning center: numenta.com/learn • HTM Studio: http://numenta.com/htm-studio • Partners: • Grok (anomalies in IT infrastructure): grokstream.com • Cortical.io (NLP): cortical.io • Contact: • Alex Lavin: alavin@numenta.com @theAlexLavin • Subutai Ahmad: sahmad@numenta.com @SubutaiAhmad • HTM Forum: discourse.numenta.org
  28. 28. BACKUP
  29. 29. § HTM Studio § Easy to use desktop application § No data upload required, no coding required § Download application at http://numenta.com/htm-studio TRY HTM ANOMALY DETECTION WITH HTM STUDIO!
  30. 30. ANOMALIES IN IT INFRASTRUCTURE Grok • Commercial server based product detects anomalies in IT infrastructure • Runs thousands of HTM anomaly detectors in real time • 10 milliseconds per input per metric, including continuous learning • No parameter tuning required • grokstream.com
  31. 31. ANOMALIES IN FINANCIALAND SOCIAL MEDIA DATA HTM for Stocks • Real-time free demo application • Continuously monitors top 200 stocks • Available on iOS App Store or Google Play Store • Open source application: github.com/numenta/numenta-apps
  32. 32. NUMENTAANOMALY BENCHMARK (NAB) • NAB: a rigorous benchmark for anomaly detection in streaming applications • Real-world benchmark data set • 58 labeled data streams (47 real-world, 11 artificial streams) • Total of 365,551 data points • Scoring mechanism • Rewards early detection • Different “application profiles” • Open resource • AGPL repository contains data, source code, and documentation • github.com/numenta/NAB • Ongoing competition to expand NAB

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