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1
Introduction
2
Sequences of Actions
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Objectives
4
Hidden Markov Model
5
Anomaly Detection
6
Identification Relevance
7
Hidden Markov Models
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Why Markov Models
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Markov Models
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Perfect Recall
11
Hidden States
12
Dependency
13
Forward Backward
14
Forward Forward
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Viterbi Algorithm
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Sequence
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Welch Algorithm
18
Smooth Out Time Series
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Smoothie Techniques
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Smoothie in Time Series
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Techniques
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Trend similarity
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Problem landscape
24
Prereq
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Download Prereq
26
Code Examples
27
Ontology
28
Documentation
29
Model Parameters
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Fit Function
31
General Sequence
Description:
Explore techniques for handling machine data in this comprehensive talk from ODSC West 2015. Dive into the challenges of working with highly time-series-centric and volatile datasets with high signal-to-noise ratios. Learn about flexible and versatile tools for tackling machine data, including Hidden Markov Models, anomaly detection, and smoothing techniques. Discover real-world applications, the reasoning behind technique selection, and how to apply these methods to practical examples. Follow along with code examples and gain insights into model parameters, fit functions, and general sequence analysis. Ideal for data scientists and analysts looking to expand their skills in processing and analyzing machine-generated data.

ODSC West 2015 - Hacking Machine Data

Open Data Science
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