Distinguish spikes from regime shifts before acting on anomalies

Monitoring a financial data stream for unusual price movements to feed into a downstream decision agent

Not all anomalies are equivalent. A single outlier value (spike) requires different handling than five consecutive anomalous values (regime shift). Acting on a spike as if it were a regime shift — or vice versa — produces opposite errors. Spikes are usually noise or data errors. Regime shifts are usually real structural changes.

During OHLC candle stream monitoring for upordn.com, price data occasionally showed single-candle outliers from thin trading periods. A naive anomaly detector flagged these with the same severity as sustained volatility periods. Downstream, this caused false positives in quiet-chart detection logic that was trying to filter out low-volatility periods.

When collecting anomalies from any stream: always apply a cluster step before surfacing results. Group anomalies that are adjacent in time (within 3 records or 60 seconds). A cluster of 1 is a spike. A cluster of 5+ is a potential regime shift. Route spikes and regime shifts to different handlers. Use the anomaly-stream-collector skill for the full implementation pattern.

The cluster window (60s, 3 records) is a heuristic and must be tuned per stream. High-frequency streams need shorter windows; low-frequency streams need longer ones. Also does not catch gradual drift — a value that shifts slowly across 100 records will never trigger a per-record anomaly flag.

What is the best statistical method for detecting gradual drift in a stream, where no single record is anomalous but the collective movement over time is?

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