refactor(topics): 멀티 에이전트용 지식 재편 — _Common(공통 기본기) + Domain_* 구조
에이전트 8종(대화형/프로그래머 C·S/디자이너/설계자/기획자/QA/PD/PM)에게 [공통 기본 능력 + 롤별 Specialty] 2층으로 지식을 주입하기 위한 재분류. 문서 내용·포맷은 무수정, 폴더 이동만 (6,372개 문서 수 보존 확인). - Topic_Programming → Domain_Programming (내부 구조 보존) - Topic_Graphic → Domain_Design - Topic_Business → Domain_Product - Topic_General → Domain_General - _Common 신설: Math(구 Topic_Math_Specialty), Reasoning(구 General/From_Thinking & Reasoning), Reasoning_Creativity(구 General/From_창의성), Communication(Poetic_Blog_Writing + From_writing) - 타 도메인의 From_* 폴더는 유지 (출처 표기일 뿐, 이미 도메인에 맞게 분류된 문서) - 빈 폴더 정리 (memory/procedures) - 에이전트→폴더 매핑은 workspace의 .astra/agent-knowledge-map.json (9개 에이전트) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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Antigravity Agent
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---
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id: wiki-2026-0508-predictive-analytics
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title: Predictive Analytics
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [Predictive Modeling, Forecasting Analytics]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.9
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verification_status: applied
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tags: [ml, analytics, forecasting, regression, time-series]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: Python
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framework: scikit-learn / XGBoost / Prophet / PyTorch Forecasting
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---
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# Predictive Analytics
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## 매 한 줄
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> **"매 historical data 로 future outcomes 예측 — regression, classification, time series 의 union"**. 1990s statistics 에서 출발해 2010s ML 으로 mainstream, 2026 currently transformer-based forecasting (TimesFM, Chronos) 이 tabular 와 sequence 에서 공존. Business intelligence, supply chain, churn, fraud 의 매 backbone.
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## 매 핵심
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### 매 problem types
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- **Regression**: continuous target (revenue, demand, price).
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- **Classification**: discrete label (churn yes/no, fraud type).
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- **Time series**: temporally indexed (sales, sensor, stock).
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- **Survival**: time-to-event (customer lifetime, equipment failure).
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- **Ranking**: ordering items (recommendation, search).
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### 매 modern stack (2026)
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- **Tabular**: XGBoost / LightGBM / CatBoost — 매 still SOTA on most tabular.
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- **Deep tabular**: TabPFN v2, FT-Transformer — 매 zero-shot tabular foundation.
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- **Time series**: Chronos (Amazon), TimesFM (Google), Moirai — 매 pretrained TS foundation models.
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- **Classic TS**: Prophet, statsforecast (AutoARIMA, ETS) — 매 baseline.
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### 매 workflow
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1. EDA + feature engineering.
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2. Train/val/test split (temporal for TS).
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3. Model selection + CV.
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4. Hyperparameter tuning (Optuna).
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5. Calibration + interpretability (SHAP).
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6. Deployment + monitoring (drift detection).
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## 💻 패턴
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### XGBoost regression baseline
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```python
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import xgboost as xgb
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from sklearn.model_selection import KFold
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from sklearn.metrics import mean_absolute_error
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import numpy as np
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X, y = load_data()
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kf = KFold(n_splits=5, shuffle=True, random_state=42)
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scores = []
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for tr, va in kf.split(X):
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model = xgb.XGBRegressor(
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n_estimators=2000, learning_rate=0.03, max_depth=6,
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subsample=0.8, colsample_bytree=0.8,
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early_stopping_rounds=50, eval_metric="mae",
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)
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model.fit(X.iloc[tr], y.iloc[tr], eval_set=[(X.iloc[va], y.iloc[va])], verbose=False)
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scores.append(mean_absolute_error(y.iloc[va], model.predict(X.iloc[va])))
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print(f"CV MAE: {np.mean(scores):.4f} +/- {np.std(scores):.4f}")
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```
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### LightGBM classification w/ early stopping
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```python
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import lightgbm as lgb
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model = lgb.LGBMClassifier(
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n_estimators=5000, learning_rate=0.02, num_leaves=63,
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min_child_samples=20, reg_alpha=0.1, reg_lambda=0.1,
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)
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model.fit(
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X_tr, y_tr,
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eval_set=[(X_va, y_va)],
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callbacks=[lgb.early_stopping(100), lgb.log_evaluation(0)],
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)
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proba = model.predict_proba(X_te)[:, 1]
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```
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### Time series with TimesFM (foundation model, zero-shot)
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```python
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import timesfm
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tfm = timesfm.TimesFm(
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hparams=timesfm.TimesFmHparams(backend="gpu", per_core_batch_size=32),
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checkpoint=timesfm.TimesFmCheckpoint(huggingface_repo_id="google/timesfm-2.0-500m"),
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)
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forecast, _ = tfm.forecast(
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inputs=[history_series], # list of np.array
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freq=[0], # 0=high freq, 1=med, 2=low
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horizon_len=96,
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)
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```
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### Prophet (interpretable seasonality)
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```python
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from prophet import Prophet
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m = Prophet(yearly_seasonality=True, weekly_seasonality=True, changepoint_prior_scale=0.05)
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m.add_country_holidays(country_name="US")
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m.fit(df) # df with ds, y columns
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future = m.make_future_dataframe(periods=90)
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fcst = m.predict(future) # yhat, yhat_lower, yhat_upper
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```
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### SHAP explainability
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```python
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import shap
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explainer = shap.TreeExplainer(model)
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shap_values = explainer.shap_values(X_te)
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shap.summary_plot(shap_values, X_te)
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shap.waterfall_plot(shap.Explanation(values=shap_values[0], base_values=explainer.expected_value, data=X_te.iloc[0]))
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```
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### Probability calibration
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```python
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from sklearn.calibration import CalibratedClassifierCV
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calibrated = CalibratedClassifierCV(base_model, method="isotonic", cv="prefit")
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calibrated.fit(X_va, y_va)
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# Brier score + reliability diagram on test
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```
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### Drift monitoring (Evidently)
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```python
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from evidently.report import Report
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from evidently.metric_preset import DataDriftPreset
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report = Report(metrics=[DataDriftPreset()])
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report.run(reference_data=X_train, current_data=X_prod)
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report.save_html("drift.html")
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```
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## 매 결정 기준
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| 상황 | Approach |
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|---|---|
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| Tabular, <1M rows | XGBoost / LightGBM |
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| Tabular, mixed features | CatBoost |
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| Zero-shot tabular | TabPFN v2 |
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| Time series, single series | Prophet / AutoARIMA |
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| Time series, many series, zero-shot | Chronos / TimesFM |
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| Need interpretability | Linear / GAM + SHAP |
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| Very large (>10M) tabular | LightGBM w/ histogram |
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**기본값**: LightGBM + Optuna + SHAP for tabular; Chronos zero-shot for new TS problems.
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## 🔗 Graph
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- 부모: [[Machine-Learning]] · [[Statistics]]
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- 변형: [[Time-Series-Analysis|Time-Series-Forecasting]] · [[Regression]]
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- Adjacent: [[XGBoost]] · [[LightGBM]] · [[SHAP]] · [[Prophet]]
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## 🤖 LLM 활용
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**언제**: business outcome forecasting, risk scoring, demand planning, anomaly detection 의 supervised learning.
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**언제 X**: causal inference (use [[Causal-Inference]]), prescriptive optimization (use [[Optimization]]).
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## ❌ 안티패턴
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- **Data leakage**: 매 future info in training features — invalidates evaluation.
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- **Random split on time series**: must use temporal split.
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- **Ignoring calibration**: probabilities used for decisions but never calibrated.
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- **No drift monitoring**: model decays silently in production.
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- **Over-engineering deep nets for small tabular**: GBDT wins under 100k rows.
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## 🧪 검증 / 중복
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- Verified (Kaggle Grandmaster patterns, Amazon Chronos paper 2024, Google TimesFM 2024).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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|---|---|
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — full predictive analytics workflow with 2026 foundation models |
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