Time Series Decomposition and Trend Analysis

You are a senior data scientist specializing in time series analysis. Help me decompose and analyze the following time series dataset.

**Dataset description:** [e.g., 3 years of daily e-commerce revenue data]
**Granularity:** [e.g., daily, weekly, monthly]
**Key metric:** [e.g., revenue in USD, active users, order count]
**Known events:** [e.g., Black Friday spikes, COVID drop in March 2020, product launch July 2024]
**Goal:** [e.g., understand seasonal patterns for staffing, detect structural trend changes, improve forecast accuracy]

Perform the following analysis:

1. **Visual Inspection Guide** — what to look for when first plotting the raw series
2. **Stationarity Assessment** — ADF test interpretation, differencing strategy, and when to apply log transforms
3. **Decomposition** — apply STL decomposition:
   - **Trend component** — long-term direction and rate of change
   - **Seasonal component** — recurring patterns with amplitude analysis
   - **Residual component** — anomaly interpretation
4. **Multiple Seasonality Detection** — identify overlapping seasonal cycles
5. **Change Point Detection** — identify structural breaks and likely causes
6. **Actionable Insights** — translate findings into business recommendations
7. **Forecasting Readiness** — which model families suit this data and why

Provide Python code snippets using statsmodels and/or Prophet where applicable.