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-time-series-analysis
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title: Time Series Analysis
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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: [Time Series, TSA, Forecasting, Temporal Analysis]
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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: [time-series, forecasting, ml, statistics]
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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: statsmodels-prophet-darts-nixtla
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---
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# Time Series Analysis
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## 매 한 줄
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> **"매 시간 축 위의 dependency가 modeling target"**. ARIMA의 classical 1970s 시대를 거쳐, 2020s에 Transformer-based foundation models (TimeGPT, Chronos, Moirai) 가 zero-shot forecasting을 가능케 했다. 매 2026 현재 hybrid (statistical + neural) 가 production default.
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## 매 핵심
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### 매 components
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- **Trend**: long-term direction.
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- **Seasonality**: periodic (daily, weekly, yearly).
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- **Cyclic**: aperiodic fluctuations.
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- **Residual**: noise / unexplained.
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### 매 stationarity
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- Strict / weak stationarity 의 distinction.
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- ADF, KPSS test 로 확인.
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- Differencing, log transform, Box-Cox 로 stationarize.
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### 매 model spectrum
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1. **Classical**: ARIMA, SARIMA, ETS (Exponential Smoothing), Holt-Winters.
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2. **ML**: XGBoost on lag features, LightGBM (M5 winner).
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3. **Deep**: LSTM, Temporal Fusion Transformer, N-BEATS, N-HiTS.
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4. **Foundation models** (2024+): TimeGPT, Chronos (Amazon), Moirai (Salesforce), TimesFM (Google).
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### 매 응용
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1. Demand forecasting (retail, supply chain).
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2. Anomaly detection (monitoring, fraud).
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3. Financial markets (volatility, price).
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4. Energy load prediction.
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## 💻 패턴
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### Pattern 1: ARIMA baseline (statsmodels)
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```python
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from statsmodels.tsa.arima.model import ARIMA
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import pandas as pd
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ts = pd.read_csv("sales.csv", parse_dates=["date"], index_col="date")["y"]
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model = ARIMA(ts, order=(2, 1, 2), seasonal_order=(1, 1, 1, 12))
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fit = model.fit()
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forecast = fit.forecast(steps=12)
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```
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### Pattern 2: Prophet (Meta)
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```python
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from prophet import Prophet
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df = pd.DataFrame({"ds": dates, "y": values})
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m = Prophet(yearly_seasonality=True, weekly_seasonality=True)
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m.add_country_holidays(country_name="KR")
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m.fit(df)
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future = m.make_future_dataframe(periods=90)
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fc = m.predict(future)
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```
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### Pattern 3: Darts (unified library, 2026 modern)
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```python
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from darts import TimeSeries
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from darts.models import TFTModel # Temporal Fusion Transformer
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series = TimeSeries.from_dataframe(df, "ds", "y")
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train, val = series.split_before(0.8)
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model = TFTModel(
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input_chunk_length=24,
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output_chunk_length=12,
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hidden_size=64,
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lstm_layers=2,
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num_attention_heads=4,
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)
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model.fit(train, val_series=val)
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pred = model.predict(n=12)
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```
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### Pattern 4: Foundation model zero-shot (Chronos)
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```python
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from chronos import ChronosPipeline
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import torch
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pipe = ChronosPipeline.from_pretrained(
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"amazon/chronos-t5-large",
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device_map="cuda",
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torch_dtype=torch.bfloat16,
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)
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context = torch.tensor(history) # no fine-tune
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forecast = pipe.predict(context, prediction_length=24, num_samples=20)
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median = forecast.median(dim=1).values
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```
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### Pattern 5: Anomaly detection (rolling z-score)
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```python
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import numpy as np
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def rolling_anomaly(ts, window=30, threshold=3.0):
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rolling_mean = ts.rolling(window).mean()
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rolling_std = ts.rolling(window).std()
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z = (ts - rolling_mean) / rolling_std
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return np.abs(z) > threshold
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```
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### Pattern 6: Backtesting (walk-forward)
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```python
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from sklearn.model_selection import TimeSeriesSplit
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tscv = TimeSeriesSplit(n_splits=5)
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scores = []
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for train_idx, test_idx in tscv.split(X):
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model.fit(X[train_idx], y[train_idx])
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pred = model.predict(X[test_idx])
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scores.append(mape(y[test_idx], pred))
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```
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### Pattern 7: Nixtla statsforecast (fast classical)
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```python
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from statsforecast import StatsForecast
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from statsforecast.models import AutoARIMA, AutoETS
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sf = StatsForecast(
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models=[AutoARIMA(season_length=12), AutoETS(season_length=12)],
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freq="M",
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n_jobs=-1,
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)
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sf.fit(df)
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forecast = sf.forecast(h=12, level=[80, 95])
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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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| <1000 points, single series | ARIMA / Prophet |
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| Many related series, hierarchical | Nixtla statsforecast (parallel) |
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| Multivariate, many covariates | TFT, N-BEATSx (Darts) |
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| Zero-shot, no training data | Chronos / TimeGPT / TimesFM |
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| Real-time anomaly | Rolling z-score / Isolation Forest |
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**기본값**: Prophet for prototype, Darts TFT or Chronos zero-shot for production.
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## 🔗 Graph
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- 부모: [[Machine Learning]] · [[Statistics]]
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- 변형: [[ARIMA]] · [[Prophet]]
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- 응용: [[Anomaly Detection]]
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- Adjacent: [[데이터 사이언스 및 ML 엔지니어링|LSTM]] · [[Transformer]] · [[Foundation Models]]
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## 🤖 LLM 활용
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**언제**: temporal data forecasting, anomaly detection, capacity planning.
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**언제 X**: cross-sectional data without time component, pure classification.
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## ❌ 안티패턴
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- **No stationarity check**: ARIMA에 non-stationary series 직접 적용.
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- **Random shuffling**: time series 의 train/test split 에 random shuffle 사용 (data leakage).
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- **MAPE on near-zero values**: division-by-zero / explosion. SMAPE 또는 MASE 사용.
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- **Ignoring seasonality**: yearly/weekly cycle 의 무시.
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- **Look-ahead bias**: future information 의 feature engineering 에 사용.
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## 🧪 검증 / 중복
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- Verified (Hyndman, "Forecasting: Principles and Practice" 3rd ed; M-competition results; Chronos paper 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 TSA spectrum (classical → foundation models) with code |
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