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Analysis

Explore Time-Based Patterns In Data

Examines how key metrics actually moved over a date range and granularity before anyone tries to forecast them. Covers overall trend direction, seasonality at weekly through annual scales, anomalies or change points, day-of-week patterns, and year-over-year growth rates, reporting the time window and magnitude behind every finding so it feeds directly into an anomaly report or forecast.

Prompt
Explore the time-based patterns hidden in my dataset:

Date column: {date_column}
Key metrics to track over time: {key_metrics}
Time range: {time_range}
Granularity available: {granularity}

Examine:
1. The overall trend (rising, falling, flat, cyclical)
2. Seasonality (weekly, monthly, quarterly, annual)
3. Notable anomalies or change points (sudden spikes, drops, or trend shifts)
4. Day-of-week or time-of-day patterns, if relevant
5. Year-over-year comparisons if multiple years exist
6. Growth-rate calculations (MoM, QoQ, YoY)

For every finding, specify the time period, the size of the effect, and whether it's a recurring pattern or a one-off event.
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