Moving Median is Robust to Anomalies

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moving-median

It can be difficult to detect the underlying trend of aAi??time series in the presence of anomalies, due to unwanted noise. Fortunately, there are techniques to take into account those anomalies, so you can work with this kind of time series. One of them is the moving median.

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Detecting Seasonality Using Fourier Transforms in R

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detect seasonality

Our brains are really fast at recognizing patterns and forms: we can often find the seasonality of a signal in under a second. It is also possible do this with mathematics using the Fourier transform.

First, we will explain what a Fourier transform is. Next, we will find the seasonality of a website from its Google Analytics pageview report using the R language.

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Anomaly Detection Using K-Means Clustering

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kmean anomaly

Monitored metrics very often exhibit regular patterns. When the values are correlated with the time of the day, it’s easier to spot anomalies, but it’s harder when they do not. In some cases, it is possible to use machine learning to differentiate the usual patterns from the unusual ones.

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Delta Time (Ii??t) for Anomaly Detection

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delta time anomaly

It isAi??common to monitor the number of events that occur in a period of time. Unfortunately, this technique isn’t fast, and can fail to detect some anomalies. The alternative is to change the problem to studyingAi??the period of time betweenAi??events.

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Anomaly Detection with the Poisson Distribution

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anomaly detection with poisson distribution

Instinctively we look at anomalies by examining the number of events during a fixed period of time. However, this method can’t achieve fast detection rates, and fails to detect some anomalies. The Poisson distribution turns the problem upside-down by looking at the period of time for a fixed number of events

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Anomaly Detection with the Normal Distribution

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anomaly in normal distribution

The normalAi??distributionAi??is theAi??holy grail of anomaly detection. Normally distributed metrics follow a set of probabilisticAi??rules. Values that follow those rules are recognized as beingAi??”normal” or “usual”, while values that break them are seen as being unusual, indicating anomalies.
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