time-series-analyst
Analyzes time-series data for patterns, trends, seasonality, and anomalies, with forecasting using statistical and machine learning methods.
npx skills add majiayu000/claude-skill-registry --skill time-series-analyst --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 Analyst This skill provides guidance for analyzing temporal data, identifying patterns, and building forecasting models. ## Core Competencies - **Decomposition**: Trend, seasonality, residual analysis - **Statistical Methods**: ARIMA, SARIMA, Exponential Smoothing - **ML Methods**: Prophet, LSTM, Transformer-based models - **Anomaly Detection**: Statistical and ML approaches ## Time Series Fundamentals ### Data Characteristics Before analysis, assess: | Property | Question | Impact | |----------|----------|--------| | Stationarity | Is mean/variance constant? | Method selection | | Seasonality | Are there repeating patterns? | Model components | | Trend | Is there long-term direction? | Differencing needs | | Frequency | What's the sampling rate? | Aggregation choices | | Missing values | Are there gaps? | Imputation needs | ### Stationarity Tests ```python from statsmodels.tsa.stattools import adfuller, kpss # Augmented Dickey-Fuller (null: non-stationary) adf_result = adfuller(series) print(f"ADF Statistic: {adf_result[0]:.4f}") print(f"p-value: {adf_result[1]:.4f}") # p < 0.05 suggests stationarity # KPSS (null: stationary) kpss_result = kpss(series, regression='c'
- Core Competencies
- Time Series Fundamentals
- Data Characteristics
- Stationarity Tests
- Making Series Stationary
- Time Series Decomposition
- Classical Decomposition
- Visualization Pattern
- Statistical Forecasting Methods
- ARIMA Model Selection
- SARIMA for Seasonal Data
- Exponential Smoothing
- ML-Based Forecasting
- Facebook Prophet
What does the time-series-analyst skill do?
Analyzes time-series data for patterns, trends, seasonality, and anomalies, with forecasting using statistical and machine learning methods.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill time-series-analyst --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.