Uncovering Anomalies: The Intriguing World of Time Series
Anomalous time series are a fascinating and crucial aspect of data analysis.
Consider the world of earthquakes, where anomalies manifest as erratic seismic signals indicating potential danger. These sudden spikes or drops in data serve as a warning sign for looming threats.
In the realm of finance, the infamous Wall Street Crash of 1929 stands out as a stark example of anomaly detection in the financial domain. Meanwhile, in engineering, spikes in signals may signify ultrasound reflections off objects, providing valuable insights.
These anecdotes underscore a common conundrum:
How do we differentiate between normal and anomalous signals when faced with a new data point?
It’s worth noting that this challenge differs slightly from detecting anomalies within a given signal—a problem with its own set of solutions. Here, the focus lies on determining whether a new signal deviates significantly from the established norms in our datasets.