# TS Core > TS Core - Skill: `ztxtech/ts-core` (Agent Skill) - Install (CLI): `npx skillmds@latest add ztxtech/ts-core` - Raw SKILL.md: https://api.skillmd.com/api/skills/ztxtech/ts-core/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: ztxtech (https://skillmd.com/u/ztxtech) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/ztxtech/ts-core --- # TS Core ## Task Recognition Identify first: forecasting / classification / anomaly detection / event detection / segmentation / imputation / representation learning. Then: univariate/multivariate, one-step/multi-step, point/interval forecast, single/multi-entity. Write time format clearly: timestamps, timezone, frequency, window, forecast step, entity granularity, exogenous variables. Prevent leakage: split, normalization, feature building, target alignment must follow time boundaries. ## Data Contract Before analysis, unify data entry into one contract: source type, time column, target column, feature columns, entity columns, frequency/timezone, data range, missing-value strategy, split method, output location. Four entry types: PDF (use `pdf-intake` read-only), table files (header/type/time-column checks), databases (query contract, reproducible middle table), code-style DataLoader (review I/O contract, check leakage). If raw directory doesn't fit project structure, copy into `data/raw_external/` or equivalent, then normalize. Downstream depends only on the normalized interface. ## Visual Analysis (Mandatory) Plot first, then judge. At minimum: - Raw time-series line plots, local zoom, entity/variable comparison - Missing values, outliers, distribution drift, regime shift markers - Rolling mean/variance, season/trend decomposition when possible - ACF/PACF, spectrum, periodicity diagnosis when possible If the figure is wrong (bad axes, hidden lines, missing legends, broken export), fix the figure first. ## Statistical / Feature Analysis Check: length/frequency stability, missing patterns, outlier ratio, stationarity signs, trend/seasonality/noise/heteroscedasticity, lag relations, cross-entity patterns. Use `tsfresh`-style features to answer: is this trend-driven, cycle-driven, event-driven, or noise-driven? ## Method Family & Latest-Method Awareness **Never default to a familiar method without checking what's current.** Before implementation: 1. Compare `direct use / zero-shot / few-shot / frozen-backbone / light adaptation / retraining` — training is NOT the default. 2. Consider at least these families in parallel: - Statistical (ARIMA, ETS, Prophet, state-space) - Traditional ML (XGBoost, LightGBM, CatBoost with lag features) - Deep learning (LSTM, TCN, Transformer variants, PatchTST, DLinear) - Pretraining / TSFM (Chronos, TimesFM, TimeGPT, Moirai, Lag-Llama) - Hybrid / rule-constrained / tool-enhanced 3. Each route must answer: why it fits, what assumptions, when it fails, engineering cost, can it be used directly/zero-shot/light-adapted. 4. If a TSFM/foundation route fails on environment/deps/weights, do NOT kill it directly. Judge workarounds: zero-shot, few-shot, frozen-backbone, lighter setup, window changes, two-stage design. 5. Use `information-collector` to search for latest methods and SOTA before committing to a route. Do not assume the method you know is the best available. ## Domain Priors (Brief) Time-series tasks appear across many domains. Recognize the domain early — it shapes data patterns, constraints, and evaluation: | Domain | Typical Patterns | Key Risks | Special Needs | |--------|-----------------|-----------|---------------| | **Energy/Power** | Strong daily/weekly cycles, weather sensitivity, holiday effects | Concept drift from policy/season changes, sensor faults | Load forecasting, demand response, renewable integration | | **Finance/Trading** | Long-term trends, volatility clustering, regime switches, fat tails | Look-ahead bias, survivorship bias, market microstructure noise | Technical indicators, factor models, event studies, risk metrics (VaR, drawdown) | | **Traffic/Transport** | Rush-hour peaks, weekend/weekday split, incident shocks | Sensor dropout, network topology effects, weather interaction | Spatiotemporal GNN, multi-sensor fusion | | **Weather/Climate** | Multi-scale periodicity, spatial correlation, extreme events | Distribution shift from climate change, measurement drift | Physical constraints, ensemble methods, downscaling | | **Healthcare/Medical** | Patient-specific baselines, intervention effects, irregular sampling | Missing not at random (MNAR), privacy constraints, small samples | Clinical validation, uncertainty quantification, causal inference | | **Retail/Sales** | Holiday spikes, promotion effects, trend + seasonality, new product cold-start | Promotion confounding, stockout censoring, returns distortion | Hierarchical forecasting, causal impact, price elasticity | | **Industrial/IoT** | Degradation trends, maintenance events, multi-sensor correlation | Sensor drift, unlabeled anomalies, concept drift from wear | Anomaly detection, remaining useful life, predictive maintenance | | **Web/CloudOps** | Bursty traffic, diurnal patterns, deployment-induced shifts | High cardinality, concept drift from releases, alert fatigue | Anomaly detection, capacity planning, root-cause analysis | For any domain: search both general TS methods AND domain-specific mechanisms. Do not treat domain keyword + "time series" as the whole first-round search. ## Forecast Validation Before accepting any forecast output: 1. **Horizon length** — output must match task requirement exactly. 2. **Output schema** — clear schema first, then generate. Label sets enforced for classification/MCQ. 3. **Numerical plausibility** — units/scale match history, no unexplained explosion/drift/negative, volatility reasonable, physical/business constraints respected. 4. **Uncertainty strategy** — when evidence is weak, prefer `Uncertain`/intervals/confidence info over hard guessing. ## Post-Experiment Analysis Do NOT look only at the main metric. Required before closeout: - SHAP / feature attribution or equivalent explanation analysis - Error distribution, residual diagnosis, failure cases - Slice/cohort analysis (time range, horizon, entity) - Statistical tests / significance when applicable - Math-modeling view: can the error be rewritten as residual modeling, error decomposition, layered/segmented models, state-switch, or constrained optimization? If any of these is missing, the experiment is not done. ## Test Loop Fixed loop: save structured results → make plots with `scripts/plot/` → visual semantic analysis → targeted retest from visual findings → self-critique → `ts-critic` review again. If one link is missing, the loop is not complete. ## Length Awareness Do not assume more history is always better. Look for the sweet spot: what short/middle/long windows each add, where noise dominates. For long history, consider: salient-subsequence retrieval, regime compression, structured summaries, parallel multi-scale windows.