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Seasonality & Trend Decomposer
Identify and quantify seasonal patterns, underlying trends, and residual noise in time-series data to support accurate forecasting.
Updated June 2026
The prompt
Analyze {{metric_name}} across {{time_period}}. Break down the time series into: 1) trend component (direction and strength), 2) seasonal component (cycle length, amplitude, phase), 3) residual/noise levels. Provide seasonality indices for each month/quarter, explain anomalies that break the pattern, and recommend smoothing or deseasonalization approaches for forecasting.
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Author
AI Khazna
License
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Security
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Type
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