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Beyond Forecasts: Unmasking Anomalies in Time Series Data with Facebook Prophet
Leveraging Prophet for Anomaly Detection
Imagine this: You are looking at a graph of your website traffic and notice a sudden, unexplained drop. Or maybe you’ve seen an unexpected spike in sales that didn’t align with any marketing campaign.
These little weirdos in your data are what we call anomalies. They might be a sign of a problem, like a server going down, or a great opportunity, like a new trend emerging.
So, what exactly is time series anomaly detection?
It’s the process of finding data points that don’t fit the expected pattern in a sequence of data collected over time.
You’re looking for the one thing that doesn’t belong. This is super important for a bunch of reasons, from catching credit card fraud and monitoring system performance to spotting unusual business activity.
But here’s the catch: time series data is tricky. It has seasons (like more sales in December), trends (like a business growing over time), and holidays that can make a data point look weird when it’s totally normal. Plus, it’s often hard to find labeled examples of what an anomaly looks like…
