2nd/10_Wiki/Topics/Topic_Programming/AI_and_ML/Time-Series-Analysis.md

id, title, category, status, canonical_id, aliases, duplicate_of, source_trust_level, confidence_score, verification_status, tags, raw_sources, last_reinforced, github_commit, tech_stack

id	title	category	status	canonical_id	aliases	duplicate_of	source_trust_level	confidence_score	verification_status	tags	raw_sources	last_reinforced	github_commit	tech_stack
wiki-2026-0508-time-series-analysis	Time Series Analysis	10_Wiki/Topics	verified	self	Time Series TSA Forecasting Temporal Analysis	none	A	0.9	applied	time-series forecasting ml statistics		2026-05-10	pending	language framework python statsmodels-prophet-darts-nixtla

Time Series Analysis

매 한 줄

"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.

매 핵심

매 components

• Trend: long-term direction.
• Seasonality: periodic (daily, weekly, yearly).
• Cyclic: aperiodic fluctuations.
• Residual: noise / unexplained.

매 stationarity

• Strict / weak stationarity 의 distinction.
• ADF, KPSS test 로 확인.
• Differencing, log transform, Box-Cox 로 stationarize.

매 model spectrum

1. Classical: ARIMA, SARIMA, ETS (Exponential Smoothing), Holt-Winters.
2. ML: XGBoost on lag features, LightGBM (M5 winner).
3. Deep: LSTM, Temporal Fusion Transformer, N-BEATS, N-HiTS.
4. Foundation models (2024+): TimeGPT, Chronos (Amazon), Moirai (Salesforce), TimesFM (Google).

매 응용

1. Demand forecasting (retail, supply chain).
2. Anomaly detection (monitoring, fraud).
3. Financial markets (volatility, price).
4. Energy load prediction.

💻 패턴

Pattern 1: ARIMA baseline (statsmodels)

from statsmodels.tsa.arima.model import ARIMA
import pandas as pd

ts = pd.read_csv("sales.csv", parse_dates=["date"], index_col="date")["y"]
model = ARIMA(ts, order=(2, 1, 2), seasonal_order=(1, 1, 1, 12))
fit = model.fit()
forecast = fit.forecast(steps=12)

Pattern 2: Prophet (Meta)

from prophet import Prophet

df = pd.DataFrame({"ds": dates, "y": values})
m = Prophet(yearly_seasonality=True, weekly_seasonality=True)
m.add_country_holidays(country_name="KR")
m.fit(df)
future = m.make_future_dataframe(periods=90)
fc = m.predict(future)

Pattern 3: Darts (unified library, 2026 modern)

from darts import TimeSeries
from darts.models import TFTModel  # Temporal Fusion Transformer

series = TimeSeries.from_dataframe(df, "ds", "y")
train, val = series.split_before(0.8)

model = TFTModel(
    input_chunk_length=24,
    output_chunk_length=12,
    hidden_size=64,
    lstm_layers=2,
    num_attention_heads=4,
)
model.fit(train, val_series=val)
pred = model.predict(n=12)

Pattern 4: Foundation model zero-shot (Chronos)

from chronos import ChronosPipeline
import torch

pipe = ChronosPipeline.from_pretrained(
    "amazon/chronos-t5-large",
    device_map="cuda",
    torch_dtype=torch.bfloat16,
)
context = torch.tensor(history)  # no fine-tune
forecast = pipe.predict(context, prediction_length=24, num_samples=20)
median = forecast.median(dim=1).values

Pattern 5: Anomaly detection (rolling z-score)

import numpy as np

def rolling_anomaly(ts, window=30, threshold=3.0):
    rolling_mean = ts.rolling(window).mean()
    rolling_std = ts.rolling(window).std()
    z = (ts - rolling_mean) / rolling_std
    return np.abs(z) > threshold

Pattern 6: Backtesting (walk-forward)

from sklearn.model_selection import TimeSeriesSplit

tscv = TimeSeriesSplit(n_splits=5)
scores = []
for train_idx, test_idx in tscv.split(X):
    model.fit(X[train_idx], y[train_idx])
    pred = model.predict(X[test_idx])
    scores.append(mape(y[test_idx], pred))

Pattern 7: Nixtla statsforecast (fast classical)

from statsforecast import StatsForecast
from statsforecast.models import AutoARIMA, AutoETS

sf = StatsForecast(
    models=[AutoARIMA(season_length=12), AutoETS(season_length=12)],
    freq="M",
    n_jobs=-1,
)
sf.fit(df)
forecast = sf.forecast(h=12, level=[80, 95])

매 결정 기준

상황	Approach
<1000 points, single series	ARIMA / Prophet
Many related series, hierarchical	Nixtla statsforecast (parallel)
Multivariate, many covariates	TFT, N-BEATSx (Darts)
Zero-shot, no training data	Chronos / TimeGPT / TimesFM
Real-time anomaly	Rolling z-score / Isolation Forest

기본값: Prophet for prototype, Darts TFT or Chronos zero-shot for production.

🔗 Graph

• 부모: Machine Learning · Statistics
• 변형: ARIMA · Prophet
• 응용: Anomaly Detection
• Adjacent: 데이터 사이언스 및 ML 엔지니어링 · Transformer · Foundation Models

🤖 LLM 활용

언제: temporal data forecasting, anomaly detection, capacity planning. 언제 X: cross-sectional data without time component, pure classification.

❌ 안티패턴

• No stationarity check: ARIMA에 non-stationary series 직접 적용.
• Random shuffling: time series 의 train/test split 에 random shuffle 사용 (data leakage).
• MAPE on near-zero values: division-by-zero / explosion. SMAPE 또는 MASE 사용.
• Ignoring seasonality: yearly/weekly cycle 의 무시.
• Look-ahead bias: future information 의 feature engineering 에 사용.

🧪 검증 / 중복

• Verified (Hyndman, "Forecasting: Principles and Practice" 3rd ed; M-competition results; Chronos paper 2024).
• 신뢰도 A.

🕓 Changelog

날짜	변경
2026-05-08	Phase 1
2026-05-10	Manual cleanup — full TSA spectrum (classical → foundation models) with code