domain-time-series
Skill8 starsChanged 4 months agoMITView on GitHub
Use for time-series tasks: forecasting (demand, sales, energy, finance), time-series classification, and anomaly detection over time (sensors, logs, monitoring). Picks classical vs ML vs deep forecasters, enforces temporal splits (no future leakage), and uses proper time-series metrics/backtesting. Triggers on 'forecast', 'predict next/future', 'time series', 'demand/sales/energy prediction', 'anomaly detection over time', 'seasonality'.
In this repository22 other files it gives its agents.
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The file
Files
Time-Series - Method Selection
Temporal order is sacred: the future must never leak into training.
Decision table
| Sub-task | Recommended (simple to advanced) | Notes |
| Univariate / few series forecasting | ETS / ARIMA / Theta / Prophet | Strong, interpretable baselines. Often beat deep on small data. |
| Many related series | LightGBM on lag/calendar features then N-BEATS/NHITS, TFT | GBT-on-lags is a very strong, cheap default. |
| Long-horizon / rich covariates | TFT / PatchTST / DeepAR | Deep only when data volume justifies it. |
| Foundation / zero-shot | TimesFM / Chronos / Moirai | Good for cold-start / many series with little history. |
| TS classification | ROCKET/MiniROCKET, InceptionTime, HIVE-COTE | ROCKET = fast strong baseline. |
| Anomaly detection | STL/residual + threshold, Isolation Forest, autoencoder/LSTM | Start simple; label scarcity is the norm. |
Temporal rigor (non-negotiable)
- Split by time: train strictly before validation before test. Use rolling/expanding-window backtesting, not random K-fold (
data-rigor-and-leakage). - No future leakage in features: lags/rolling stats use only past data; align target horizon carefully; no target-derived or post-hoc features.
- Fit scalers/encoders on the training window only, refit as the window rolls.
- Respect gaps/frequency: handle missing timestamps, irregular sampling, and known future covariates (holidays) vs unknown.
Evaluation
- Metrics: MAE / RMSE / MAPE / sMAPE / MASE (MASE compares to a naive baseline - always include a naive/seasonal-naive baseline).
- Report per-horizon error and prediction intervals (coverage), not just a point metric.
- Backtest across multiple origins; a single split is not enough.
- Improve: use
accuracy-improvement-loop(better features/lags, hierarchical reconciliation, ensembling classical+ML, then deep).
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