Anamoly Reads

Ref - Link

Summary

• Point Anomalies - Value is far outside the entirety of the data set
• Conditional Outliers - With respect to context, Same value may not be anamoly in another time 
• Collective Outliers - Set of 1 or more points that deviate from dataset

Telemanom 

Paper - Machine Learning for Time Series Anomaly Detection

Key Notes

• Clustering methods do not require the data to be labeled, making it a good fit for our unsupervised task. Very sensitive to outlier data points

Two-Step Process

• The number of clusters can be set to 2 (one anomalous and one normal)
• Summarized by taking averages across an interval of one hour
• Rolling Window Sequences

On the Nature and Types of Anomalies: A Review of Deviations in Data

Patent - FAST AUTOMATED DETECTION OF ( 56 ) SEASONAL PATTERNS IN TIME SERIES DATA WITHOUT PRIOR KNOWLEDGE OF SEASONAL PERIODICITY 

Key Notes

• Calculate Automatic correlation based on timeseries values
• Identify local maxima
• The seasonal trend identification module
• Data store for Normal data, Anamoly data
• Scoring module
• Human in loop feedback system

Sklearn Models for Supervised Anomaly Detection. Some popular scikit-learn models for supervised anomaly detection include:

• KNeighborsClassifier
• SVC (SVM classifier)
• DecisionTreeClassifier
• RandomForestClassifier
• Interquartile Range
• Isolation Forest
• Median Absolute Deviation
• K-Nearest Neighbours

More Reads

Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding

Machine Learning for Time Series Anomaly Detection

Anomaly Detection Learning Resources.

Anomaly Detection in Time Series Data Using LSTMs and Automatic Thresholding

SYSTEMAND METHOD FOR TRANSFORMING OBSERVED METRICS INTO DETECTED AND SCORED ANOMALIES

	#Z Score
	#Mean, Variance, Standard Deviation
	#Example - Data 2,4,6
	#Sum = 12
	#Average = 12/3 = 4
	#Variance = squares of differences between all numbers and means.
	#2 = (2-4) = 2*2 = 4
	#4 = (4-4) = 0 = 0
	#6 = (6-4) = 3*2 = 4
	#variance = 8/3 = 2.66
	#standard deviation = sqrt(2.66)
	#z score = (x-mean)/std
	#Ref - https://www.geeksforgeeks.org/z-score-for-outlier-detection-python/
	import numpy as np
	import math
	x = [10, 12, 22, 45, 36, 14, 10, 125]
	mean = np.mean(x)
	std = np.std(x)
	print('mean of the dataset is', mean)
	print('std. deviation is', std)
	print('1st SD',mean - 1 * std, mean + 1 * std)
	print('2nd SD',mean - 2 * std, mean + 2 * std)
	print('3rd SD',mean - 3 * std, mean + 3 * std)
	for i in data:
	z = (i-mean)/std
	print(z)

view raw Zscorebasics.py hosted with ❤ by GitHub

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