Anomaly 2 benchmark results series#
Anomaly 2 benchmark results free#

With default parameters, the LOF algorithm doesn’t tag some of the spikes but it detects two datapoints at the start of time-series which were previously never considered as outliers. index ) plot ( s1, anomaly = lof_outliers1, ts_linewidth = 1, ts_markersize = 3, anomaly_color = 'red', figsize = ( 20, 10 ), anomaly_tag = 'marker', anomaly_markersize = 5 ) #Imports for context (not needed if you're following the notebook, as they've been imported previously)įrom sklearn.neighbors import LocalOutlierFactor from adtk.visualization import plot clf = LocalOutlierFactor ( contamination = 'auto' ) lof_outliers1 = clf.

Anomaly 2 benchmark results free#

Here, we’re going to use the default auto but if you know approximately the proportion of outliers, feel free to provide it. This will determin the boundaries that the LOF builds.

The only parameter that you might want to provide is the contamination rate which helps the algorithm to understand how many outliers you expect to find. Thankfully, sklearn’s API is intuitive and well documented, so fitting the model is quite easy. We’ll start with the density-based models. Afterwards, these models are going to be benchmarked on a variety of labelled time-series datasets. In this section I’m going to show you how you can fit these models on web-traffic data and how the results can be visualised. For GLOSH algorithm we can use the HDBSCAN implementation. I’m going to use the sklearn implementation of LOF and their implementation of Isolation Forest. If you haven’t followed Part 1, you can follow the setup instructions there and come back here after installing the dependencies, downloading the data, and reading it in. Setup is similar to the last part, so you can simply continue working the in the same notebook. Outliers are those with the smallest depth score because they were among the easiest to be isolated (e.g. It then measures how deep a tree has to be for a data point to be isolated, averages this depth score across the trees, and find the outliers. holidays) that can look abnormal.įinally, Isolation Forest (Liu et al., 2008) is a tree-based method that builds a number of decision trees and parses through the data chossing the best splits (based on entropy or information gain). This means that they will flag not only the most obvious spikes and drops but they will also detect outliers in specific contexts (e.g. These two approaches work in practice because they can detect local outliers. GLOSH (Campello et al., 2015) is further improvement of this approach and it based on hierarchical estimates. Clusters with very high density are considered to be normal, whereas points which belong to the regions with lowest density are considered to be outliers. Proximity is usually measured by Euclidean distance but it can be other metric as well. LOF algorithm (Breunig et al., 2000) gives each point a density score which is determined by its proximity to other data points. The two first models are based on the concepts of distance and density.

Global Local Outlier Score in Hierarchies (GLOSH).

Here, I’m going to cover 3 new algorithms: In these cases, it’s good to have additional algorithms in your toolkit that use somewhat different approaches in detecting the anomalies. type of products, weather) that are relevant to the task or when these algorithms are unable to detect some outliers. However, there might be cases when we need to add some additional context variables (e.g. They utilise some of the time-series’ properties (moving averages, auto-regression, seasonality) and they are quite efficient and accurate.

Anomaly 2 benchmark results series#

In part 1 of this blog series I’ve talked about more classical approaches to anomaly detection.