Unlocking the Mysteries of Anomalous Time Series Data
When it comes to anomalies in time series data, it’s no laughing matter. Picture this – in the realm of earthquakes, anomalies manifest as erratic seismic signals, hinting at potential disaster lurking beneath the surface.
Transition to the financial domain, and we recall the catastrophic Wall Street Crash of 1929 – a glaring instance of anomalies wreaking havoc. Even in engineering, spikes in signals can indicate anything from ultrasound reflections to structural integrity issues.
These real-life scenarios highlight a common conundrum:
How do we discern abnormal signals from the norm when a new data point enters the fray?
It’s important to note that this challenge differs slightly from detecting anomalies within an existing signal. Instead, the focus is on determining if a new signal deviates significantly from the established “normal” dataset.