IQR technique doesn’t work in the following scenarios Q75, q25 = np.percentile(x, ) iqr = q75 - q25 Drawbacks Np.percentile is baked functionality in Python A quartile is what divides the data into three points and four intervals. In simple words, any dataset or any set of observations is divided into four defined intervals based upon the values of the data and how they compare to the entire dataset. IQR is a concept in statistics that is used to measure the statistical dispersion and data variability by dividing the dataset into quartiles. One of the most popular ways is the Interquartile Range (IQR). The simplest approach to identifying irregularities in data is to flag the data points that deviate from common statistical properties of distribution, including mean, median, mode, and quartiles.
Their features differ from normal instances significantly.Īnomaly Detection Techniques Interquartile Range (IQR). Anomalies only occur very rarely in the data. Typically the anomalous items will translate to some kind of problem such as credit card fraud, network intrusion, medical diagnostic, system health monitor.Īnomaly detection works on two basic premise Therefore, all data points outside these 1.5*IQR values are flagged as outliers.Anomaly detection is the identification of rare items, events, or observations that raise suspicions by differing significantly from the majority of the data. The whiskers are generally extended into 1.5*IQR distance on either side of the box. In the boxplot below, the length of the box is IQR, and the minimum and maximum values are represented by the whiskers. 4) Boxplotīoxplot provides a better graphical representation of IQR, but also provides additional information. IQR can be used standalone for outlier detection, but boxplots below use the same algorithmic theory and are probably more intuitive than IQR. The theory behind anomaly detection using IQR is that, if a data point is too far from the 1st and 3rd quartile, it probably is an outlier. So the Interquartile Range is the distance between 1st and 3rd quartiles. In other words, you can split data into 3 quartiles - 1st, 2nd and 3rd (the 2nd quartile has a name for it- the median). The mid-points of each of these halves is called a quartile. If you arrange data from small to large, the mid-point is called the median. While complex algorithms can be inevitable to use, sometimes simple techniques are more than enough to serve the purpose.īelow is a primer on five statistical techniques. The purpose of this article is to summarise some simple yet powerful statistical techniques that can be readily used for initial screening of outliers. Credit card fraud detection is the most cited one but in numerous other cases anomaly detection is an essential part of doing business such as detecting network intrusion, identifying instrument failure, detecting tumor cells etc.Ī range of tools and techniques are used to detect outliers and anomalies, from simple statistical techniques to complex machine learning algorithms, depending on the complexity of data and sophistication needed. 
There is also a huge industrial application of anomaly detection. Sometimes we filter completely legitimate outlier data points and remove them to ensure greater model performance. As a data scientist when we make data preparation we take great care in understanding if there is any data point unexplained, which may have entered erroneously. There are numerous reasons why understanding and detecting outliers are important. In this article, however, I am using these terms interchangeably. That is to say, all anomalies are outliers but not necessarily all outliers are anomalies. However, when this outlier is completely unexpected and unexplained, it becomes an anomaly. An “outliers’ generally refers to a data point that somehow stands out from the rest of the crowd. In data science, “Outlier”, “Anomaly” and “Fraud” are often synonymously used, but there are subtle differences. To counter these kinds of financial losses a huge amount of resources are employed to identify frauds and anomalies in every single industry. In the UK fraudulent credit card transaction losses were estimated at more than USD 1 billion in 2018. According to a Nilson Report, the amount of global credit card fraud alone was USD 7.6 billion in 2010. Anomaly and fraud detection is a multi-billion-dollar industry.
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