… or the method you probably never heard of. Maybe I am wrong but this method is the most popular and at the same time highly underestimated. So we

Outlier Detection in R: Hampel Filter for time series

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2024-09-19 10:30:07

… or the method you probably never heard of. Maybe I am wrong but this method is the most popular and at the same time highly underestimated. So we are going to fix this gap today.

In the industry of outlier detection, there are still many tips and tricks. Just like we dissected Grubbs’ Test and the Tukey Method, it’s time to see how the Hampel Filter can help us clean our data.

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Time series data can be tricky; it’s a sequence of data points indexed in time order, often fraught with noise, seasonality, and yes — those pesky outliers. Whether you’re tracking stock prices, monitoring weather patterns, or analyzing web traffic, outliers can throw a wrench in your analysis. And here comes the Hampel Filter, your temporal data’s sanitizer.

The Hampel Filter identifies outliers based on the median absolute deviation (MAD), a measure less affected by outliers in the data than the standard deviation. It’s particularly useful in situations where data may be skewed or contain several unusual points that could skew the mean and standard deviation.