# Statistical Analysis Identifying Trends

> Sub-skill of statistical-analysis: Identifying Trends (+2).

- Skill: `vamseeachanta/statistical-analysis-identifying-trends` (Agent Skill)
- Install (CLI): `npx skillmds@latest add vamseeachanta/statistical-analysis-identifying-trends`
- Raw SKILL.md: https://api.skillmd.com/api/skills/vamseeachanta/statistical-analysis-identifying-trends/raw
- Safety review: PASS (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Data & Analytics
- Author: vamseeachanta (https://skillmd.com/u/vamseeachanta)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/vamseeachanta/statistical-analysis-identifying-trends

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# Identifying Trends (+2)

## Identifying Trends


**Moving averages** to smooth noise:
```python
# 7-day moving average (good for daily data with weekly seasonality)
df['ma_7d'] = df['metric'].rolling(window=7, min_periods=1).mean()

# 28-day moving average (smooths weekly AND monthly patterns)
df['ma_28d'] = df['metric'].rolling(window=28, min_periods=1).mean()
```

**Period-over-period comparison**:
- Week-over-week (WoW): Compare to same day last week
- Month-over-month (MoM): Compare to same month prior
- Year-over-year (YoY): Gold standard for seasonal businesses
- Same-day-last-year: Compare specific calendar day

**Growth rates**:
```
Simple growth: (current - previous) / previous
CAGR: (ending / beginning) ^ (1 / years) - 1
Log growth: ln(current / previous)  -- better for volatile series
```


## Seasonality Detection


Check for periodic patterns:
1. Plot the raw time series -- visual inspection first
2. Compute day-of-week averages: is there a clear weekly pattern?
3. Compute month-of-year averages: is there an annual cycle?
4. When comparing periods, always use YoY or same-period comparisons to avoid conflating trend with seasonality


## Forecasting (Simple Methods)


For business analysts (not data scientists), use straightforward methods:

- **Naive forecast**: Tomorrow = today. Use as a baseline.
- **Seasonal naive**: Tomorrow = same day last week/year.
- **Linear trend**: Fit a line to historical data. Only for clearly linear trends.
- **Moving average forecast**: Use trailing average as the forecast.

**Always communicate uncertainty**. Provide a range, not a point estimate:
- "We expect 10K-12K signups next month based on the 3-month trend"
- NOT "We will get exactly 11,234 signups next month"

**When to escalate to a data scientist**: Non-linear trends, multiple seasonalities, external factors (marketing spend, holidays), or when forecast accuracy matters for resource allocation.