[timeseries-decomposition] Time Series Decomposition Plot

[MarkusNeusinger]

A time series decomposition plot showing the breakdown of a time series into its trend, seasonal, and residual components. Commonly used in time series analysis to understand underlying patterns.

[claude]

✅ Spec ID: timeseries-decomposition

Rationale: Uses timeseries-decomposition following the pattern of analysis-focused plot types, describing the breakdown of time series into trend, seasonal, and residual components displayed as stacked subplots.

Add label approved to begin generation.

[github-actions]

✅ Specification Ready: timeseries-decomposition

PR: #2999

---

specification.md

timeseries-decomposition: Time Series Decomposition Plot

Description

A time series decomposition plot displays a time series broken down into its constituent components: the original series, trend, seasonal pattern, and residual noise. Each component is shown as a separate subplot stacked vertically, sharing a common time axis. This visualization is essential for understanding the underlying structure of time series data and identifying patterns that may not be visible in the raw series.

Applications

• Analyzing sales data to separate long-term growth trends from seasonal holiday patterns
• Decomposing temperature records to isolate climate trends from annual cycles
• Understanding stock market behavior by separating systematic patterns from random fluctuations
• Preprocessing time series for forecasting by examining component predictability

Data

• date (datetime) - Sequential timestamps at regular intervals (daily, monthly, etc.)
• value (numeric) - Continuous measurements or observations at each timestamp
• Size: 100-1000 points (minimum 2 full seasonal cycles for meaningful decomposition)
• Example: Monthly airline passengers over 10 years, daily retail sales over 2 years

Notes

• Display four vertically stacked subplots: Original, Trend, Seasonal, and Residual
• Use consistent x-axis (time) across all subplots for easy comparison
• Label each subplot clearly with component name
• Consider using statsmodels seasonal_decompose or similar for decomposition
• Additive decomposition is typical; multiplicative may be needed for data with proportional seasonality
• Include light grid lines to aid reading values across time

---

Next: Add approved label to merge this specification to main.

---

🤖 spec-create workflow

[github-actions]

🚀 Specification Merged!

Specification: timeseries-decomposition
PR: #2999 (merged)

The specification is now in main and synced to PostgreSQL.

---

Next: Add generate:{library} labels to start generating implementations.

Available libraries: matplotlib, seaborn, plotly, bokeh, altair, plotnine, pygal, highcharts, letsplot

Or use workflow dispatch for bulk operations.

---

🤖 spec-create workflow