😱3 types of data anomalies
Data analysts and machine learning professionals often detect anomalies in data – detection that is not detected to pattern detection and detection. There are 3 types of anomalies:
• point anomaly, when one data point (observation) in the data set is far from the sources of the others and represents an extreme, unevenness or deviation that occurs randomly and is not related to the overall load in the data. The point anomaly also eliminates the global outlier because it is significantly different from the rest of the dataset.
• context anomaly, when a particular instance is revealed to be observable from the context. For example, in the case of time series data, such as recording a certain amount over time, the context is temporal. Data points that are very different from other data in the same sense are due to contextual outliers. For example, when the number of cars passing through the checkpoint on the border of the region in March, on average, is 1 thousand over the past 20 years. And in June, when the vacation period begins, this number reaches 8 thousand. If at the beginning of March it is 9 thousand, it will be considered an anomaly, and in the summer it will not be an anomaly. It's common for retail to see shoppers pop up during the holiday season. But a sharp increase in sales outside of holidays or sales can be called a contextual outlier.
• Collective anomaly, where a group of correlated, interrelated, or sequential instances is significantly different from the rest of the data, ie these data points are judged to be anomalous. For time series data, this may look like typical peaks and troughs occurring outside of the time period when the seasonal sequence is normal, or as a set of time series that are in outlier conditions. For example, at the same time, along with a large number of companies, there is a drop in sales, although before that there was an upward trend.
https://medium.com/datadailyread/types-of-data-anomalies-2f6fb1747eb1