book

Time Series Indexing

Name: Time Series Indexing
Author: Mihalis Tsoukalos
ISBN: 9781838821951

by Mihalis Tsoukalos

June 2023

Intermediate to advanced

248 pages

5h 41m

English

Packt Publishing

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Time Series Indexing
ContributorsAbout the authorAbout the reviewer
Preface
Who this book is forWhat this book coversTo get the most out of this bookDownload the example code filesDownload the color images Conventions usedGet in touchShare your thoughtsDownload a free PDF copy of this book
Chapter 1: An Introduction to Time Series and the Required Python Knowledge
Technical requirementsUnderstanding time seriesTime series are everywhereEssential definitionsTime series data miningComparing time seriesThe Euclidean distanceThe Chebyshev distanceWhat is an index and why do we need indexing?The Python knowledge that we are going to needTiming Python codeAn introduction to AnacondaThe required Python packagesSetting up our environmentPrinting package versionsCreating sample dataPublicly available time series dataHow time series are processedReading time series from diskIs all data numeric?Do all lines have the same amount of data?Creating subsequencesVisualizing time seriesWorking with the Matrix ProfileExploring the MPdist distanceSummaryResources and useful linksExercises
Chapter 2: Implementing SAX
Technical requirementsThe required theoryWhy do we need SAX?NormalizationVisualizing normalized time seriesAn introduction to SAXThe cardinality parameterThe segments parameterHow to manually find the SAX representation of a subsequenceΗow can we divide 10 data points into 3 segments?Reducing the cardinality of a SAX representationDeveloping a Python packageThe basics of Python packagesThe SAX Python packageWorking with the SAX packageComputing the SAX representations of the subsequences of a time seriesCounting the SAX representations of a time seriesThe tsfresh Python packageCreating a histogram of a time seriesCalculating the percentiles of a time seriesSummaryUseful linksExercises
Chapter 3: iSAX – The Required Theory
Technical requirementsBackground informationTrees and binary treesUnderstanding how iSAX worksThe cardinality parameterThe segments parameterThe threshold parameterComputing the normalized mean valuesHow big can an iSAX index get?What happens when there is no space left for adding more subsequences to an iSAX index?How iSAX is constructedHow iSAX is searchedPromotion strategySplitting nodesManually constructing an iSAX indexUpdating the counting.py utilitySummaryUseful linksExercises
Chapter 4: iSAX – The Implementation
Technical requirementsA quick look at the iSAX Python packageThe class for storing subsequencesThe class for iSAX nodesThe class for entire iSAX indexesExplaining the missing partsExploring the remaining filesThe tools.py fileThe variables.py fileThe sax.py fileUsing the iSAX Python packageReading the iSAX parametersHow to process subsequences to create an iSAX indexCreating our first iSAX indexCounting the subsequences of an iSAX indexHow long does it take to create an iSAX index?Dealing with iSAX overflowsSummaryUseful linksExercises
Chapter 5: Joining and Comparing iSAX Indexes
Technical requirementsHow the sliding window size affects the iSAX construction speedChecking the search speed of iSAX indexesJoining iSAX indexesImplementing the joining of iSAX indexesExplaining the Python codeUsing the Python codeWe have a long list of Euclidean distances, so what?Saving the outputFinding iSAX nodes without a matchWriting Python testsWhat are we going to test?Comparing the number of subsequencesChecking the number of node splitsAll Euclidean distances are 0Running the testsSummaryUseful linksExercises
Chapter 6: Visualizing iSAX Indexes
Technical requirementsStoring an iSAX index in JSON formatDownloading the JavaScript code locallyRunning the code locallyVisualizing an iSAX indexVisualizing iSAX as a treeTrying something radicalMore iSAX index visualizationsUsing icicle plotsVisualizing iSAX as a Collapsible TreeSummaryUseful linksExercises
Chapter 7: Using iSAX to Approximate MPdist
Technical requirementsUnderstanding the Matrix ProfileWhat does the Matrix Profile compute?Manually computing the exact Matrix ProfileComputing the Matrix Profile using iSAXWhat happens if there is not a valid match?Calculating the errorApproximate Matrix Profile implementationComparing the accuracy of two different parameter setsUnderstanding MPdistHow to compute MPdistManually computing MPdistCalculating MPdist using iSAXImplementing the MPdist calculation in PythonUsing the approximate Matrix Profile wayUsing the join of two iSAX indexesUsing the Python codeComparing the accuracy and the speed of the methodsSummaryUseful linksExercises
Chapter 8: Conclusions and Next Steps
Concluding all that we have learned so farOther variations of iSAXInteresting research papers on time seriesInteresting research papers on databasesUseful booksUseful books on databasesBuilding a strong computer science backgroundBooks on UNIX and LinuxBooks on the Python programming languageSummaryUseful linksExercises

Index
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Content preview from Time Series Indexing

Preface

The title of the book you are reading is Time Series Indexing, which should hint at its contents.

The time series index discussed and explored in this book is called iSAX. iSAX is considered one of the best indexes for time series, which is the main reason for choosing it. Besides implementing iSAX and the SAX representation as Python 3 packages, this book shows how to work with time series at the subsequence level and understand the information presented in academic research papers.

But the book does not stop here as it presents Python scripts for getting to know your time series data better and code for visualizing time series data and iSAX indexes to better understand the data as well as the structure of a particular iSAX index.

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ISBN: 9781838821951

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Time Series Indexing

by Mihalis Tsoukalos

Preface

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.