time-series-forecaster
Time series forecasting with ARIMA, Prophet, LSTM, and statistical methods. Activates for "time series", "forecasting", "predict future", "trend analysis", "seasonality", "ARIMA", "Prophet", "sales forecast", "demand prediction", "stock prediction". Handles trend decomposition, seasonality detection, multivariate forecasting, and confidence intervals with SpecWeave increment integration.
Profile →npx skills add majiayu000/claude-skill-registry --skill time-series-forecaster-anton-abyzov-specweave-03c28426 --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 Forecaster ## Overview Specialized forecasting pipelines for time-dependent data. Handles trend analysis, seasonality detection, and future predictions using statistical methods, machine learning, and deep learning approaches—all integrated with SpecWeave's increment workflow. ## Why Time Series is Different **Standard ML assumptions violated**: - ❌ Data is NOT independent (temporal correlation) - ❌ Data is NOT identically distributed (trends, seasonality) - ❌ Random train/test split is WRONG (breaks temporal order) **Time series requirements**: - ✅ Temporal order preserved - ✅ No data leakage from future - ✅ Stationarity checks - ✅ Autocorrelation analysis - ✅ Seasonality decomposition ## Forecasting Methods ### 1. Statistical Methods (Baseline) **ARIMA (AutoRegressive Integrated Moving Average)**: ```python from specweave import TimeSeriesForecaster forecaster = TimeSeriesForecaster( method="arima", increment="0042" ) # Automatic order selection (p, d, q) forecaster.fit(train_data) # Forecast next 30 periods forecast = forecaster.predict(horizon=30) # Generates: # - Trend analysis # - Seasonality decomposition # - Autocorrelation plots (ACF, PACF) # - Residual diagn
- Overview
- Why Time Series is Different
- Forecasting Methods
- 1. Statistical Methods (Baseline)
- 2. Prophet (Facebook)
- 3. Deep Learning (LSTM/GRU)
- 4. Multivariate Forecasting
- Time Series Best Practices
- 1. Temporal Train/Test Split
- 2. Stationarity Testing
- 3. Seasonality Detection
- 4. Cross-Validation for Time Series
- 5. Handling Missing Data
- Common Time Series Patterns
Create time series forecast Evaluate forecast Decompose time series
What does the time-series-forecaster skill do?
Time series forecasting with ARIMA, Prophet, LSTM, and statistical methods. Activates for "time series", "forecasting", "predict future", "trend analysis", "seasonality", "ARIMA", "Prophet", "sales forecast", "demand prediction", "stock prediction". Handles trend decomposition, seasonality detection, multivariate forecasting, and confidence intervals with SpecWeave increment integration.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill time-series-forecaster-anton-abyzov-specweave-03c28426 --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.