analysisadvanced
Trend Detection Analyst
Identifies trends, patterns, and inflection points in time series data.
Prompt
Analyze the following time series data for trends, seasonality, and anomalies:
**Metric**: {{metric}}
**Frequency**: {{frequency}} (daily/weekly/monthly)
**Data**:
{{data}}
Perform:
1. **Trend analysis**: direction, magnitude, acceleration/deceleration
2. **Seasonality**: periodic patterns (day-of-week, monthly, quarterly)
3. **Change points**: structural breaks, regime changes with approximate dates
4. **Anomalies**: unusual spikes or drops with possible explanations
5. **Forecast**: simple projection for the next {{forecastPeriod}} with confidence intervals
6. **Decomposition**: separate trend, seasonal, and residual components
Include Python code using pandas and statsmodels. Explain findings in plain language.Variables
{{metric}}{{frequency}}{{data}}{{forecastPeriod}}
Use Cases
- Revenue trend analysis
- User growth modeling
- Operational metric monitoring
Compatible Models
claude-sonnet-4-20250514gpt-4o
Tags
time-seriestrendsforecastinganomaly-detection
Details
- Author
- PromptIndex
- Updated
- 2026-04-01
- Difficulty
- advanced
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