Summary
The video delves into the diverse types of outliers in time series data, such as global, contextual, and collective outliers. Global outliers are data points that notably differ from the majority, like sudden spikes in sales. Contextual outliers are those that deviate based on specific situations, such as erratic weather patterns affecting data. Collective outliers involve a group of data points standing out collectively. Detecting these outliers is crucial for improving business operations and decision-making.
Types of Outliers in Time Series Data Sets
Outlines the different types of outliers or anomalies that occur in time series data sets, including global outliers, contextual outliers, and collective outliers.
Global Outliers
Explains global outliers as data points with values that significantly deviate from the rest of the data set, using examples like spikes in sales or extreme changes in usage patterns.
Contextual Outliers
Describes contextual outliers as data points that differ significantly based on context, such as changes in weather patterns or pricing errors in specific situations.
Collective Outliers
Discusses collective outliers that occur when a group of data points collectively stand out, even though individual points may not be outliers on their own. It highlights the importance of detecting these outliers for business operations.
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