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Time-Series Decomposition Brief
Decompose {{metric}} into trend, seasonality, and residual components with business interpretation.
Updated June 2026
The prompt
Analyze the time series for {{metric}} over {{time_period}}: 1. Apply seasonal decomposition (STL or classical method) 2. Quantify: - Trend strength (direction, rate of change) - Seasonality pattern (amplitude, frequency, phase) - Residual variance (noise, unexplained variance) 3. Identify inflection points (anomalies, level shifts) 4. Translate components into business drivers: - Trend: underlying growth/decline + external factors - Seasonality: user behavior, calendar, campaigns - Residual: one-off events, data quality issues 5. Forecast next {{forecast_periods}} with confidence band Deliver as visual + written narrative.
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Author
AI Khazna
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Security
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