Visuele beoordeling van seizoenspatronen

De eerste stap bij het analyseren van tijdreeksen is het maken van grafische samenvattingen die inzicht geven in belangrijke kenmerken zoals trend, seizoensinvloeden en mogelijke afwijkingen. In deze oefening gebruik je verschillende grafieken om de nitrate-concentraties als tijdreeks te verkennen en de periode van eventuele seizoenspatronen te bepalen.

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Introductie tot anomaliedetectie in R

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# View contents of dataset
head(river)

# Show the time series of nitrate concentrations with time
plot(___ ~ ___, data = river, type = ___)

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Deze oefening maakt deel uit van de cursus

Introductie tot anomaliedetectie in R

SkillTag.level.intermediateSkillTag.label

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In this chapter, you'll learn how numerical and graphical summaries can be used to informally assess whether data contain unusual points. You'll use a statistical procedure called Grubbs' test to check whether a point is an outlier, and learn about the Seasonal-Hybrid ESD algorithm, which can help identify outliers when the data are a time series.

Exercise 1: Wat bedoelen we als we het over anomalieën hebben?Exercise 2: Anomalietypen herkennen Exercise 3: De riviernitraatgegevens verkennen Exercise 4: Extremen testen met de Grubbs-test Exercise 5: Visuele controle op normaliteit Exercise 6: Grubbs-test Exercise 7: Meerdere uitschieters opsporen met de Grubbs-toets Exercise 8: Afwijkingen in tijdreeksen Exercise 9: Visuele beoordeling van seizoenspatronen

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Exercise 10: Seasonal Hybrid ESD-algoritme Exercise 11: De output van Seasonal-Hybrid ESD interpreteren Exercise 12: Seasonal-Hybrid ESD versus Grubbs' test

In this chapter, you'll learn how to calculate the k-nearest neighbors distance and the local outlier factor, which are used to construct continuous anomaly scores for each data point when the data have multiple features. You'll learn the difference between local and global anomalies and how the two algorithms can help in each case.

Exercise 1: k-nearest neighbors distance score Exercise 2: Exploring wine Exercise 3: kNN distance matrix Exercise 4: kNN distance score Exercise 5: Visualizing kNN distance Exercise 6: Standardizing features Exercise 7: Appending the kNN score Exercise 8: Visualizing kNN distance score Exercise 9: Local outlier factor Exercise 10: LOF calculation Exercise 11: LOF visualization Exercise 12: LOF vs kNN

k-nearest neighbors distance and local outlier factor use the distance or relative density of the nearest neighbors to score each point. In this chapter, you'll explore an alternative tree-based approach called an isolation forest, which is a fast and robust method of detecting anomalies that measures how easily points can be separated by randomly splitting the data into smaller and smaller regions.

Exercise 1: Isolation trees Exercise 2: Fit and predict with an isolation tree Exercise 3: Score interpretation Exercise 4: Isolation forest Exercise 5: Fit an isolation forest Exercise 6: Checking convergence Exercise 7: Visualizing the isolation score Exercise 8: A grid of points Exercise 9: Prediction over a grid Exercise 10: Anomaly contours

You've now been introduced to a few different algorithms for anomaly scoring. In this final chapter, you'll learn to compare the detection performance of the algorithms in instances where labeled anomalies are available. You'll learn to calculate and interpret the precision and recall statistics for an anomaly score, and how to adapt the algorithms so they can accommodate data with categorical features.

Exercise 1: Labeled anomalies Exercise 2: Thyroid data Exercise 3: Visualizing thyroid disease Exercise 4: Anomaly score Exercise 5: Measuring performance Exercise 6: Binarized scores Exercise 7: Cross-tabulate binary scores Exercise 8: Thyroid precision and recall Exercise 9: Working with categorical features Exercise 10: Converting character to factor Exercise 11: Isolation forest with factors Exercise 12: LOF with factors Exercise 13: Wrap-up