In a previous blog I wrote about 6 potential applications of time series.... To recap, they are the following:

- Trend analysis
- Outlier/anomaly detection
- Examining shocks/unexpected variation
- Association analysis
- Forecasting
- Predictive analytics

Here I am focusing on outlier and anomaly detection. Important to note that outliers and anomalies can be synonymous, but there are few differences, although I am not going into those nuances.

**WHAT IS AN OUTLIER?**

In terms of definition, an outlier is an observation that significantly differs from other observations of the same feature. If a time series is plotted, outliers are usually the unexpected spikes or dips of observations at given points in time. A temporal dataset with outliers have several characteristics:

- There is systematic pattern (which is deterministic) and some variation (which is stochastic)
- Only a few data points are outliers
- Outliers are significantly different from the rest of the data

**WHY DETECT OUTLIERS?**

Broadly for two reasons:

(1) In business applications the project managers should know if an outlier represents an error. Or are there specific reasons they should be concerned of (if undesired) or excited about (if desired).

(2) In research and statistical modeling projects outliers impact model performance. So they are removed during model fitting to enhance prediction accuracy.

**REAL WORLD APPLICATION DOMAINS (few of many)**

- Financial market: Price manipulation, fraudulent transactions and fraud detection in banking and stock market exchange
- Credit card: fraud detection algorithms detect any unusual/fraudulent financial transactions or credit card theft
- Computer network: Detecting network intrusion based on anomalous traffic in computer networks
- Process industries: Anomaly detection in pulp and paper industries and other process industries
- Aviation: Aircraft sensor monitoring to observe any potential malfunction
- Healthcare: Abnormal patient conditions based on reading electrocardiogram (ECG) recordings of heart beat pulses
- Recommender systems: Detection of attacks in Recommendation Systems to alter recommendations
- Hydrology: Real time monitoring of hydrological monitoring and management of water resources
- Web analytics: Detecting unexpected growth or drop in website visit and monitoring any significant statistical variations and anomalies
- Weather forecast: Real-time weather monitoring based on satellite, radar and ground measurements to detect extreme events
- Acoustic monitoring: real-time acoustic monitoring of oceanic activities for research and other applications such as environmental conservation
- Geology: Observation of earthquake and seismic activities due to anthropogenic causes such as nuclear tests.
- Astronomy: In astronomy detecting outliers in the observation of features and characteristics of stars and galaxies. Most famous of all is probably the recent gravitational waves detection.

LIGO measurement of the gravitational waves at the Livingston (right) and Hanford (left) detectors,

compared with the theoretical predicted values (Source: Wikipedia)

**TOOLS AND METHODS (few of many)**

- STL: This decomposes time series data into 3 components: seasonality, trend and residue
- Generalized Extreme Student Deviation
- ARIMA: There is a R package called tsoutliers to do exactly this.
- AnomalyDetection R package
- Mean Absolute Deviation (MAD) for real time monitoring of streaming data
- Exponential smoothing: By observing the deviation of unsmoothed data with the smoothed ones
- Sliding window-based forecasting method: This approach uses a forecasting model built using past data within a given window and predicts a future value. If an observed value significantly differs from predicted value, it’s an outlier.
- Peer Group Analysis

© 2019 Data Science Central ® Powered by

Badges | Report an Issue | Privacy Policy | Terms of Service

**Most Popular Content on DSC**

To not miss this type of content in the future, subscribe to our newsletter.

**Technical**

- Free Books and Resources for DSC Members
- Learn Machine Learning Coding Basics in a weekend
- New Machine Learning Cheat Sheet | Old one
- Advanced Machine Learning with Basic Excel
- 12 Algorithms Every Data Scientist Should Know
- Hitchhiker's Guide to Data Science, Machine Learning, R, Python
- Visualizations: Comparing Tableau, SPSS, R, Excel, Matlab, JS, Pyth...
- How to Automatically Determine the Number of Clusters in your Data
- New Perspectives on Statistical Distributions and Deep Learning
- Fascinating New Results in the Theory of Randomness
- Long-range Correlations in Time Series: Modeling, Testing, Case Study
- Fast Combinatorial Feature Selection with New Definition of Predict...
- 10 types of regressions. Which one to use?
- 40 Techniques Used by Data Scientists
- 15 Deep Learning Tutorials
- R: a survival guide to data science with R

**Non Technical**

- Advanced Analytic Platforms - Incumbents Fall - Challengers Rise
- Difference between ML, Data Science, AI, Deep Learning, and Statistics
- How to Become a Data Scientist - On your own
- 16 analytic disciplines compared to data science
- Six categories of Data Scientists
- 21 data science systems used by Amazon to operate its business
- 24 Uses of Statistical Modeling
- 33 unusual problems that can be solved with data science
- 22 Differences Between Junior and Senior Data Scientists
- Why You Should be a Data Science Generalist - and How to Become One
- Becoming a Billionaire Data Scientist vs Struggling to Get a $100k Job
- Why do people with no experience want to become data scientists?

**Articles from top bloggers**

- Kirk Borne | Stephanie Glen | Vincent Granville
- Ajit Jaokar | Ronald van Loon | Bernard Marr
- Steve Miller | Bill Schmarzo | Bill Vorhies

**Other popular resources**

- Comprehensive Repository of Data Science and ML Resources
- Statistical Concepts Explained in Simple English
- Machine Learning Concepts Explained in One Picture
- 100 Data Science Interview Questions and Answers
- Cheat Sheets | Curated Articles | Search | Jobs | Courses
- Post a Blog | Forum Questions | Books | Salaries | News

**Archives**: 2008-2014 | 2015-2016 | 2017-2019 | Book 1 | Book 2 | More

**Most popular articles**

- Free Book and Resources for DSC Members
- New Perspectives on Statistical Distributions and Deep Learning
- Time series, Growth Modeling and Data Science Wizardy
- Statistical Concepts Explained in Simple English
- Machine Learning Concepts Explained in One Picture
- Comprehensive Repository of Data Science and ML Resources
- Advanced Machine Learning with Basic Excel
- Difference between ML, Data Science, AI, Deep Learning, and Statistics
- Selected Business Analytics, Data Science and ML articles
- How to Automatically Determine the Number of Clusters in your Data
- Fascinating New Results in the Theory of Randomness
- Hire a Data Scientist | Search DSC | Find a Job
- Post a Blog | Forum Questions

## You need to be a member of Data Science Central to add comments!

Join Data Science Central