using modes for time series classification - rohit chatterjee

Copyright © 2013 Microland LimitedJuly 2015

Using Modes for Time Series Analysis

Extraordinary.Everyday.

Copyright © 2013 Microland Limited 2

About this Talk

Time Series Classification

IT Operations Analytics

Pattern Recognition

Anomaly Detection


About Me

Computer Science

• UT Austin

Math• UT Austin• UW

Madison

Finance• Interactive

Brokers

Software Development

• Tally Solutions

Data Science

• Microland

Rohit [email protected]

mailto:[email protected]


IT Operations Management

IT Systems are monitored continuously

Large quantities of machine data generated

Scope for automated analysis


Time Series in IT Monitoring Data


Goal

We Need Features

We Want to Classify These Series


Looking for Features

MeanVarianceMaxMinPeak-to-peakSlope

Not enough

We could try:


Limitations of Mean and Variance

Both these series have the same value for σ / μ

But they look very different!


Consider the Mode

Single Mode Two Modes


Modal “Strength”

One mode, almost 100% of the series

Each mode is at around 50%


The “Run Ratio”

-The run is broken- The longer run is around

80% of the modal series

-The first mode’s run is unbroken- 100% of the modal series

(Similarly for the second mode…)


The “Run Ratio w.r.t the Parent”

80% of the entire window Only 50% of the entire window


Using the Run Ratio

The mode in each graph has

a strength of98%

Vs.

This one has a larger run ratio


What if There is No Mode?


The Difference Series

d(n) = s(n) – s(n-1)


The Difference Series of a Step Graph


A Signature


Also, We’re Hiring [email protected]

mailto:[email protected]

using modes for time series classification - rohit chatterjee

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