time-series-decomposer
Decompose time series into trend, seasonal, and residual components. Use for forecasting, pattern analysis, and seasonality detection.
Profile →npx skills add majiayu000/claude-skill-registry --skill time-series-decomposer --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Time Series Decomposer Extract trend, seasonal, and residual components from time series data with visualization and basic forecasting. ## Features - **Decomposition**: Additive and multiplicative models - **Trend Extraction**: Moving averages, polynomial fitting - **Seasonality Detection**: Auto-detect and extract periodic patterns - **Residual Analysis**: Identify anomalies in residuals - **Visualization**: Component plots, ACF/PACF - **Basic Forecasting**: Trend extrapolation, seasonal naive ## Quick Start ```python from ts_decomposer import TimeSeriesDecomposer decomposer = TimeSeriesDecomposer() decomposer.load_csv("sales.csv", date_col="date", value_col="revenue") # Decompose result = decomposer.decompose(period=12) # Monthly seasonality print(f"Trend strength: {result['trend_strength']:.2f}") print(f"Seasonal strength: {result['seasonal_strength']:.2f}") # Plot components decomposer.plot_components("decomposition.png") ``` ## CLI Usage ```bash # Basic decomposition python ts_decomposer.py --input data.csv --date date --value sales --period 12 # Multiplicative model python ts_decomposer.py --input data.csv --date date --value sales --period 12 --model multiplicative # With
- Features
- Quick Start
- CLI Usage
- API Reference
- TimeSeriesDecomposer Class
- Decomposition Models
- Additive Model
- Multiplicative Model
- Output Format
- Decomposition Result
- Trend Analysis
- Seasonality Analysis
- Period Detection
- Anomaly Detection
Basic decomposition python ts_decomposer.py --input data.csv --date date --value sales --period 12 Multiplicative model python ts_decomposer.py --input data.csv --date date --value sales --period 12 --model multiplicative With forecast python ts_decomposer.py --input data.csv --date date --value sales --period 12 --forecast 6 Auto-detect period python ts_decomposer.py --input data.csv --date date --value sales --auto-period Generate plots python ts_decomposer.py --input data.csv --date date --value sales --period 12 --plot components.png
What does the time-series-decomposer skill do?
Decompose time series into trend, seasonal, and residual components. Use for forecasting, pattern analysis, and seasonality detection.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill time-series-decomposer --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.