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Ray Richardson, Chief Technology Officer at Simularity at MLconf SEA - 5/01/15


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Practical Predictive Analytics on Time Series Data using SAX: The potential to do machine learning on the data generated by connected sensors is a key factor that is driving the spread of the Internet of Things. Predictive analytics on time series data can be used to anticipate adverse events, enable early-warning systems, improve outcomes, reduce costs, and increase efficiency. In this talk you’ll learn how to use Symbolic Aggregate approXimation (SAX) to determine normal behavior, recognize behavior that is anomalous, quantify it, and classify it based on known patterns.

In this presentation, Ray Richardson will walk you through the basics of SAX, and cover a predictive maintenance example in detail. One of the key advantages to using SAX is that it yields an explainable model. When the result of an analysis is designed to get someone to take an action, the importance of having an explainable model should not be underestimated. Key takeaways from this talk include:

- two methods to determine what normal is
- how to do time series classification
- how to predict time to failure
- open source SAX tools

Published in: Technology
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Ray Richardson, Chief Technology Officer at Simularity at MLconf SEA - 5/01/15

  1. 1. © Copyright 2015 Simularity. All Rights Reserved Ray Richardson, Founder & CTO | Practical Predictive Analytics on Time Series Data using SAX MLConf Seattle, May 1, 2015
  2. 2. © Copyright 2015 Simularity. All Rights Reserved 2 Anomaly Detection  A time series anomaly is simply an unusual subsequence of the series  “Unusual” will be taken to mean “improbable” !  The degree of anomaly is isomorphic with the improbability of the subsequence !  Probability is not defined for Time Series !  Probability can be defined for Symbols  Mapping a time series to a symbol may allow us to assign a probability to the time series subsequence  This involves mapping the time series subsequence to a symbol in some Symbol Space
  3. 3. © Copyright 2015 Simularity. All Rights Reserved 33 Symbolic Representation  All data in a modern computer is in a Symbolic Representation !  Integers, Floating point numbers and Strings are all symbols, and are all composed of bytes  Anomaly detection requires a special kind of symbol – one from a Finite Symbol Space !  This means there are a finite number of symbols available
  4. 4. © Copyright 2015 Simularity. All Rights Reserved 44 Finite Symbol Spaces  For our purposes, a Finite Symbol Space is defined by 2 attributes !  An Alphabet, from which components are drawn !  A Symbol Length, defining the fixed number of components of the symbol  Thus, if we define the alphabet as a..d and a length of 4, a legitimate symbol might be abcd  Another legitimate symbol might be 10:15, where 10 is the row of a matrix and 15 is the column !  The size of the matrix must be constant  Fixed point numbers are drawn from a Finite Symbol Space if there is a lower and upper bound
  5. 5. © Copyright 2015 Simularity. All Rights Reserved 55 Why Finite Symbol Spaces?  A Finite Symbol Space allows us to compute a (perhaps naïve) probability of seeing a particular symbol !  The number of possible symbols is al where a is the cardinality of the alphabet and l is the length of the symbol !  Perhaps naïve due to the fact that some symbols may never appear •  In some symbolic representations of time series aaaa and dddd represent the same series  We can compute a probability of seeing a symbol if they are random – it’s the reciprocal of size of the symbol space
  6. 6. © Copyright 2015 Simularity. All Rights Reserved 66 Time Series  A time series is a sequence of pairs !  Each pair consists of a Time Index and a Value !  The Time Index may be implied if there is a constant difference between values  The time series can be segmented into “Windows” which represent the time series between 2 Time Indices  Symbols can represent Windows! !  Because symbols in a Finite Symbol Space have a probability, we can think of the probability of a time series !  Symbols are easy to store and manipulate– each symbol can be represented as an integer
  7. 7. © Copyright 2015 Simularity. All Rights Reserved 77 Normalizing Time Series   A time series window can be put into a “normal form” called PAA (Piecewise Aggregate Approximation).   The PAA consists of K floating point values which represent the aggregate value of the times series over fixed time spans   Each value is the average of the readings that fall into each “box” !  Each box is a time window with a start and end derived by segmenting the time series window into K windows
  8. 8. © Copyright 2015 Simularity. All Rights Reserved 88 The Symbolic Representation Of Time Series  A number of algorithms exist to represent time series as symbols in a Finite Symbol Space !  These algorithms are often though of as “Feature Reducers”  Self Organizing Maps are a traditional form of Feature Reducer  SAX (Symbolic Aggregate approXimation) is another, designed specifically for time series  There are many other ways to reduce a time series to symbol !  As long as the symbol is drawn from a Finite Symbol Space, the technique described here will work baabccbc
  9. 9. © Copyright 2015 Simularity. All Rights Reserved 99 What is SAX?  SAX is a methodology for reducing a time series window to a symbol  The technique was developed by Dr. Eamonn Keogh et al. at the University of California at Riverside in the early 2000’s  It has since drawn a great deal of attention in the world of time series analysis
  10. 10. © Copyright 2015 Simularity. All Rights Reserved 1010 What’s a SAX Word?  A SAX word is the symbol generated by the SAX algorithm  It is defined by a SAX Alphabet and a length !  The SAX Alphabet is traditionally represented by letters, and its components are referred to as “SAX Letters” !  The size of the alphabet is typically small – this is particularly important for anomaly detection  When we write out a description of a SAX word, we typically use a string like representation, such as “abcdefg” !  SAX letters don’t have to be letters – implementations often use numbers based at zero, however, we often display them as letters
  11. 11. © Copyright 2015 Simularity. All Rights Reserved 1111 Building A SAX Word  Convert the Time Series Window to a PAA of the length of the SAX word, and Z-normalize the PAA !  Which mean and standard deviation are used for normalization will affect the outcome  Compute the SAX letter by dividing the Standard Normal Distribution into K regions of equal area under the curve and assigning each component of the PAA a letter from the SAX Alphabet corresponding to the region indexed by the PAA value  Repeating for each value of the PAA yields a SAX word of equivalent length to the PAA
  12. 12. © Copyright 2015 Simularity. All Rights Reserved How do we obtain SAX? First convert the time series to PAA representation, then convert the PAA to symbols It takes linear time 0 20 40 60 80 100 120 C C Slide by Eamonn Keogh and Jessica Lin. Used with permission. 0 -- 0 20 40 60 80 100 120 b b b a cc c a baabccbc
  13. 13. © Copyright 2015 Simularity. All Rights Reserved 1313 Encoding Magnitude And Slope  The Magnitude and slope can be encoded in a SAX word  The Magnitude (mean) can be Z-normalized over the entire space of the time series, and divided into SAX letters !  These letters need not be from the same alphabet as the SAX word which represents the shape, we just need to consider the alphabet size when computing the size of the Finite Symbol Space  Slope can be encoded by dividing 180º into equal spaces, and assigning each space to a letter !  The slope can be determined by a number of methodologies
  14. 14. © Copyright 2015 Simularity. All Rights Reserved 1414 Computing The Anomaly  We need a data structure, which uses SAX words as an index, and stores the number of times we have seen each SAX word, as well as the total number windows we’ve seen  Due to the fact that our SAX words are of a fixed length and alphabet, we know the total number of possible SAX words  Tries are one choice of data structure !  Allow for quick access  Converting the SAX word to a number, which is an array index is another !  Requires exponentiation
  15. 15. © Copyright 2015 Simularity. All Rights Reserved 1515 Computing The Anomaly  The procedure for examining a window !  Convert the window into a SAX word !  Lookup the current count for that SAX word and increment it !  Compute a metric which determines how anomalous the window is using 3 values – The total number of windows, the number of instances of this SAX word, and the size of the Finite Symbol Space of SAX words !  Compare the result of the metric with a predetermined threshold to decide whether or not this window is anomalous  This procedure is repeated for constantly incoming Time Series Windows
  16. 16. © Copyright 2015 Simularity. All Rights Reserved 1616 The Metric  Once we have determined the values, we need to turn them into a metric which tells us how anomalous a window is  The metric should discriminate !  We should be able to discriminate between multiple levels of anomaly values  The metric should be easy to compute !  Embedded applications may not have complex math libraries which allow for complicated computation  The metric should reflect the real world
  17. 17. © Copyright 2015 Simularity. All Rights Reserved 1717 The Metric – P-Values  P-Values seem like a good metric !  Expressed as a probability, they have a connection to the real world  Unfortunately, P-Values closely approach zero and one once the number of samples gets significant !  This makes it difficult to set an “anomaly threshold” !  This sets a hard criterion for an anomaly
  18. 18. © Copyright 2015 Simularity. All Rights Reserved 1818 The Metric – Log-Likelihood Ratio  The Log-Likelihood ratio is perhaps a better choice of metric !  Scaling the ratio between -1.0 and 1.0 gives a manageable value !  Even extremely unlikely events can be discriminated  Reversing the sign of the scaled log-likelihood ratio gives values that are easier to understand  Use the likelihood function for a binomial distribution !  The number of trials is the Total Windows !  The number of successes is the occurrence of this Window !  The Probability is the Symbol Probability  The log likelihood is particularly useful as it accounts for the significance of the data i.e. the number of samples  Like P-Values, it requires a floating point library
  19. 19. © Copyright 2015 Simularity. All Rights Reserved 1919 The Metric – Rate Ratio  The rate ratio is the number of times more likely the event is observed to have occurred, than would be predicted by random chance !  Smaller values mean more anomalous – less than 1 implies less likely than chance !  The reciprocal of the rate ratio gives an anomaly score which increases !  Uses observed probabilities  Doesn’t require math harder than division  Doesn’t account for significance – significance has to be accounted for by some other means
  20. 20. © Copyright 2015 Simularity. All Rights Reserved 2020 Other Means Of Symbolizing  SAX may not always be the best way to reduce a window to a symbol !  SAX reduces resolution equally across all its members !  Tiny, but important variations will be lost  Self Organizing Maps can also be used !  They require more computation, but don’t reduce resolution !  Self Organizing Maps can encode magnitude directly
  21. 21. © Copyright 2015 Simularity. All Rights Reserved 2121 Using Self Organizing Maps   Self Organizing Maps (SOMs) are (typically) a grid of vectors, which can be thought of as weights or prototypes !  The SOM algorithm adjusts the prototypes based on training data   To operate the SOM, a Window vector is compared to each of the prototypes – the best matching one “wins” and the symbol associated with the window is the row:column of the matching grid   The row:column is then used to index the count of how many times that prototype has been seen.   We now have the 3 values for computing the metric
  22. 22. © Copyright 2015 Simularity. All Rights Reserved 2222 Predicting Events  A set of time series may be used to predict events !  We look for the correlation between the symbols representing the time series windows and Events which happen in the future  This can be used to categorize Events according to an Event Signature !  Event signatures imply outcomes at a particular time index
  23. 23. © Copyright 2015 Simularity. All Rights Reserved 2323 A Concrete Example  The SMART data on hard drives can be used to predict failures !  Simularity used 53 of the sensors to test for anomalies and predict failures  Information from nearly 400 hard drives was used to “train” the anomaly detector  Once trained, the system was used to identify Event Signatures which indicated failure  The time series in the system were reduced to SAX words, and correlated with a single event, failure (all that was known)  This can then be used to predict failure
  24. 24. © Copyright 2015 Simularity. All Rights Reserved 2424 Event Signatures For Failure Prediction Notice there are two different event signatures for these failing drives
  25. 25. © Copyright 2015 Simularity. All Rights Reserved 2525 Credit  This technique is similar, although not identical, to the TARZAN methodology outlined by Eamonn Keogh and Jessica Lin !  It and other work pertaining to SAX is available here:  Self Organizing Maps were invented by Teuvo Kohonen
  26. 26. © Copyright 2015 Simularity. All Rights Reserved 2626 Source Code  Simularity maintains a GitHub repository of open-source software, including an implementation of SAX suitable for using with the techniques described here
  27. 27. 1160, Brickyard Cove Road, Suite 200 Point Richmond, CA 94801 United States + 1 678-488-8857 THANK YOU @rayrichardson