菜单
Use this Skill for time series analysis: ARIMA/SARIMA identification (ACF/PACF, AIC), seasonal decomposition (STL), VAR, GARCH volatility, and forecast evaluation.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
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直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/wookat/ai-research-skills --skill time-series-analysis命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
Lightweight pattern-driven research ideation with no script dependencies - generates 5-10 candidate ideas from a direction or gap matrix using proven ideation patterns, each with mechanism, falsification plan, and minimal validation experiment. Use for quick brainstorming of research ideas or when the scripted idea-spark skill is unavailable or not yet set up; prefer idea-spark for a single deeply-grounded reviewer-defensible idea card. Do not use for judging an existing idea (use idea-critic) or checking novelty (use scoop-check).
Generate ONE reviewer-defensible, implementable research idea with a concrete method and falsification plan from a stated research direction. Use when the user asks for a research idea, novelty analysis, bottleneck diagnosis, or paper-shape suggestion. Skip code review, debugging, and unconstrained brainstorming without research context.
Search papers across arXiv, DBLP, OpenAlex, OpenReview, Semantic Scholar, and Crossref for a given query and year range, using ./scripts/search_papers.py. Use when the user asks to find papers, related work, prior art, or recent publications on a specific topic, especially when they mention a date range or specific venues like NeurIPS, ICLR, or ICML.
基于 SOC 职业分类
正在显示 SKILL.md
| name | time-series-analysis |
| description | Use this Skill for time series analysis: ARIMA/SARIMA identification (ACF/PACF, AIC), seasonal decomposition (STL), VAR, GARCH volatility, and forecast evaluation. |
| tags | ["mathematics","time-series","ARIMA","VAR","GARCH","forecasting"] |
| version | 1.0.0 |
| authors | [{"name":"awesome-rosetta-skills contributors","github":"@xjtulyc"}] |
| license | MIT |
| platforms | ["claude-code","codex","gemini-cli","cursor"] |
| dependencies | {"python":["statsmodels>=0.14","pmdarima>=2.0","arch>=6.0","pandas>=1.5","matplotlib>=3.6","scipy>=1.9"]} |
| last_updated | 2026-03-17 |
| status | stable |
TL;DR — Full pipeline from stationarity testing and ACF/PACF identification through ARIMA/SARIMA fitting, STL decomposition, VAR impulse response, GARCH volatility modelling, and rigorous forecast evaluation (MASE, DM test).
Use this Skill when you need to:
| Task | Tool |
|---|---|
| Univariate forecasting | statsmodels ARIMA / pmdarima auto_arima |
| Seasonal decomposition | statsmodels STL |
| Multivariate forecasting | statsmodels VAR |
| Volatility modelling | arch GARCH |
| Model selection | AIC, BIC, out-of-sample MASE |
A time series is weakly stationary if its mean and autocovariance structure do not change over time. Most classical methods require stationarity:
d=1) to remove stochastic trends.D=1 at lag s) to remove seasonal unit roots.p AR lags identified from PACF cutting off after lag p.d differencing order from unit-root tests.q MA lags identified from ACF cutting off after lag q.SARIMA(p,d,q)(P,D,Q)[s].A system of k variables, each regressed on p lags of all k variables.
Lag order p chosen by AIC/BIC. Impulse response functions (IRF) trace the dynamic
effect of a one-unit shock to variable j on all other variables over h horizons.
Conditional variance: h_t = omega + alpha * e_{t-1}^2 + beta * h_{t-1}.
alpha + beta < 1 ensures covariance stationarity.
News impact curve: how a return shock of size e affects next-period variance.
conda create -n ts python=3.11 -y
conda activate ts
pip install statsmodels>=0.14 pmdarima>=2.0 arch>=6.0 \
pandas>=1.5 matplotlib>=3.6 scipy>=1.9
python -c "import statsmodels, pmdarima, arch; print('OK')"
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from statsmodels.tsa.stattools import adfuller, kpss
from statsmodels.graphics.tsaplots import plot_acf, plot_pacf
np.random.seed(42)
# ── Generate a non-stationary series with trend + noise ─────────────────────────
n = 300
t = np.arange(n)
series = 0.05 * t + np.random.randn(n).cumsum() * 0.5 # random walk + drift
ts = pd.Series(series, index=pd.date_range("2010-01-01", periods=n, freq="W"))
def run_stationarity_tests(x: pd.Series, label: str = "series") -> dict:
"""
Run ADF and KPSS stationarity tests and return a summary dict.
ADF H0: unit root present (reject -> stationary)
KPSS H0: series is stationary (reject -> non-stationary)
"""
adf_stat, adf_p, adf_lags, _, adf_cv, _ = adfuller(x, autolag="AIC")
kpss_stat, kpss_p, kpss_lags, kpss_cv = kpss(x, regression="c", nlags="auto")
result = {
"label" : label,
"adf_stat" : adf_stat,
"adf_p" : adf_p,
"kpss_stat": kpss_stat,
"kpss_p" : kpss_p,
"adf_cv_5pct" : adf_cv["5%"],
"kpss_cv_5pct" : kpss_cv["5%"],
"likely_stationary": (adf_p < 0.05) and (kpss_p > 0.05),
}
print(f"[{label}] ADF stat={adf_stat:.3f} p={adf_p:.4f} "
f"(5% cv={adf_cv['5%']:.3f})")
print(f"[{label}] KPSS stat={kpss_stat:.3f} p={kpss_p:.4f} "
f"(5% cv={kpss_cv['5%']:.3f})")
print(f"[{label}] Likely stationary: {result['likely_stationary']}")
return result
# Test levels
print("=== Level series ===")
r0 = run_stationarity_tests(ts, "levels")
# Test first difference
ts_diff = ts.diff().dropna()
print("\n=== First difference ===")
r1 = run_stationarity_tests(ts_diff, "first_diff")
# ── ACF / PACF plots ─────────────────────────────────────────────────────────────
fig, axes = plt.subplots(2, 2, figsize=(12, 6))
plot_acf(ts, lags=30, ax=axes[0, 0], title="ACF — Levels")
plot_pacf(ts, lags=30, ax=axes[0, 1], title="PACF — Levels")
plot_acf(ts_diff, lags=30, ax=axes[1, 0], title="ACF — First Diff")
plot_pacf(ts_diff,lags=30, ax=axes[1, 1], title="PACF — First Diff")
fig.tight_layout()
fig.savefig("acf_pacf.png", dpi=150)
print("\nSaved acf_pacf.png")
import warnings
from statsmodels.tsa.arima.model import ARIMA
from statsmodels.tsa.statespace.sarimax import SARIMAX
import pmdarima as pm
np.random.seed(0)
# ── Synthetic ARIMA(2,1,1) series ────────────────────────────────────────────────
from statsmodels.tsa.arima_process import ArmaProcess
ar_coefs = np.array([1, -0.6, -0.3]) # AR polynomial (includes lag-0 = 1)
ma_coefs = np.array([1, 0.4]) # MA polynomial
arma_process = ArmaProcess(ar_coefs, ma_coefs)
data_stationary = arma_process.generate_sample(nsample=400)
data_nonstationary = np.cumsum(data_stationary) # integrate once -> I(1)
dates = pd.date_range("2010-01-01", periods=len(data_nonstationary), freq="M")
ys = pd.Series(data_nonstationary, index=dates)
# ── Manual ARIMA(2,1,1) ──────────────────────────────────────────────────────────
train, test = ys[:-24], ys[-24:]
with warnings.catch_warnings():
warnings.simplefilter("ignore")
manual_model = ARIMA(train, order=(2, 1, 1))
manual_fit = manual_model.fit()
print(manual_fit.summary())
# Static 24-step forecast with confidence intervals
forecast = manual_fit.get_forecast(steps=24)
fc_mean = forecast.predicted_mean
fc_ci = forecast.conf_int(alpha=0.05)
fig, ax = plt.subplots(figsize=(10, 4))
ax.plot(train[-48:], label="Train (last 48)")
ax.plot(test, label="Actual test", color="black")
ax.plot(fc_mean, label="ARIMA(2,1,1) forecast", color="red")
ax.fill_between(fc_ci.index, fc_ci.iloc[:, 0], fc_ci.iloc[:, 1],
alpha=0.2, color="red", label="95% CI")
ax.legend()
ax.set_title("ARIMA(2,1,1) Forecast")
fig.tight_layout()
fig.savefig("arima_forecast.png", dpi=150)
# ── Auto ARIMA via pmdarima ───────────────────────────────────────────────────────
print("\nRunning auto_arima...")
auto_model = pm.auto_arima(
train,
d=1, # force one differencing (already know it's I(1))
start_p=0, max_p=4,
start_q=0, max_q=4,
seasonal=False,
information_criterion="aic",
stepwise=True,
trace=True,
error_action="ignore",
suppress_warnings=True,
)
print(f"\nBest auto_arima order: {auto_model.order}")
auto_fc, auto_ci = auto_model.predict(n_periods=24, return_conf_int=True)
auto_fc_series = pd.Series(auto_fc, index=test.index)
# ── Forecast evaluation ───────────────────────────────────────────────────────────
def mase(actual: np.ndarray, forecast: np.ndarray, insample: np.ndarray) -> float:
"""Mean Absolute Scaled Error (scale-free, handles zeros)."""
naive_mae = np.mean(np.abs(np.diff(insample)))
mae = np.mean(np.abs(actual - forecast))
return mae / naive_mae if naive_mae > 0 else np.inf
def mape(actual: np.ndarray, forecast: np.ndarray) -> float:
"""Mean Absolute Percentage Error."""
mask = actual != 0
return np.mean(np.abs((actual[mask] - forecast[mask]) / actual[mask])) * 100
manual_mase = mase(test.values, fc_mean.values, train.values)
auto_mase = mase(test.values, auto_fc, train.values)
print(f"\nManual ARIMA(2,1,1) MASE: {manual_mase:.4f}")
print(f"Auto ARIMA {auto_model.order} MASE: {auto_mase:.4f}")
from statsmodels.tsa.seasonal import STL
from statsmodels.tsa.vector_ar.var_model import VAR
# ── STL decomposition ─────────────────────────────────────────────────────────────
# Generate monthly series with trend + seasonality
n = 120
t = np.arange(n)
seasonal_component = 3 * np.sin(2 * np.pi * t / 12)
trend_component = 0.1 * t
noise = np.random.randn(n) * 0.5
y_seasonal = pd.Series(
trend_component + seasonal_component + noise,
index=pd.date_range("2014-01-01", periods=n, freq="ME"),
)
stl = STL(y_seasonal, period=12, robust=True)
stl_result = stl.fit()
fig = stl_result.plot()
fig.set_size_inches(10, 7)
fig.suptitle("STL Decomposition", y=1.01)
fig.tight_layout()
fig.savefig("stl_decomposition.png", dpi=150)
print("Saved stl_decomposition.png")
# Seasonal strength: 1 - Var(remainder) / Var(seasonal + remainder)
rem = stl_result.resid
seas = stl_result.seasonal
seasonal_strength = max(0, 1 - np.var(rem) / np.var(seas + rem))
print(f"Seasonal strength: {seasonal_strength:.4f}")
# ── VAR(p) model ─────────────────────────────────────────────────────────────────
# Bivariate VAR: US Industrial Production and Unemployment Rate (synthetic)
np.random.seed(1)
n_var = 200
# Simulate a simple bivariate AR(1) system
B = np.array([[0.7, -0.2], [0.1, 0.8]]) # coefficient matrix
eps = np.random.multivariate_normal([0, 0], [[1, 0.3], [0.3, 1]], n_var)
Y = np.zeros((n_var, 2))
for t_ in range(1, n_var):
Y[t_] = B @ Y[t_-1] + eps[t_]
df_var = pd.DataFrame(Y, columns=["y1", "y2"],
index=pd.date_range("2005-01", periods=n_var, freq="ME"))
# ── Lag selection ─────────────────────────────────────────────────────────────────
model_var = VAR(df_var)
lag_result = model_var.select_order(maxlags=8)
print("\nVAR lag selection:")
print(lag_result.summary())
best_lag = lag_result.aic
# ── Fit VAR ───────────────────────────────────────────────────────────────────────
var_fit = model_var.fit(best_lag)
print(var_fit.summary())
# ── Granger causality test ────────────────────────────────────────────────────────
gc_test = var_fit.test_causality("y1", "y2", kind="f")
print(f"\nGranger causality y2 -> y1: F={gc_test.test_statistic:.4f}, p={gc_test.pvalue:.4f}")
# ── Impulse response functions ────────────────────────────────────────────────────
irf = var_fit.irf(periods=12)
fig_irf = irf.plot(orth=True)
fig_irf.set_size_inches(8, 5)
fig_irf.tight_layout()
fig_irf.savefig("var_irf.png", dpi=150)
print("Saved var_irf.png")
# ── 12-step ahead forecast ────────────────────────────────────────────────────────
var_fc = var_fit.forecast(df_var.values[-best_lag:], steps=12)
print("\nVAR 12-step forecast (first 3 rows):")
print(pd.DataFrame(var_fc[:3], columns=["y1_hat", "y2_hat"]))
from arch import arch_model
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
np.random.seed(42)
# ── Simulate GARCH(1,1) returns ──────────────────────────────────────────────────
n_garch = 1000
omega, alpha, beta = 0.0001, 0.1, 0.85
h = np.zeros(n_garch)
r = np.zeros(n_garch)
h[0] = omega / (1 - alpha - beta)
for t in range(1, n_garch):
h[t] = omega + alpha * r[t-1]**2 + beta * h[t-1]
r[t] = np.sqrt(h[t]) * np.random.randn()
returns = pd.Series(r * 100, name="returns") # scale to percentage returns
# ── Fit GARCH(1,1) ───────────────────────────────────────────────────────────────
garch_model = arch_model(returns, vol="Garch", p=1, q=1, dist="normal", mean="Zero")
garch_fit = garch_model.fit(disp="off")
print(garch_fit.summary())
params = garch_fit.params
print(f"\nEstimated omega={params['omega']:.6f} alpha={params['alpha[1]']:.4f} "
f"beta={params['beta[1]']:.4f}")
print(f"alpha + beta = {params['alpha[1]'] + params['beta[1]']:.4f} "
f"(< 1 -> covariance stationary)")
# ── Conditional volatility plot ──────────────────────────────────────────────────
cond_vol = garch_fit.conditional_volatility
fig, axes = plt.subplots(2, 1, figsize=(10, 6), sharex=True)
axes[0].plot(returns, color="grey", linewidth=0.5, label="Returns")
axes[0].set_title("Returns")
axes[1].plot(cond_vol, color="darkred", linewidth=0.8, label="Conditional vol")
axes[1].set_title("GARCH(1,1) Conditional Volatility")
for ax in axes:
ax.legend(loc="upper right")
fig.tight_layout()
fig.savefig("garch_volatility.png", dpi=150)
print("Saved garch_volatility.png")
# ── 10-step ahead volatility forecast ───────────────────────────────────────────
garch_fc = garch_fit.forecast(horizon=10, reindex=False)
vol_fc = np.sqrt(garch_fc.variance.iloc[-1])
print("\n10-step ahead conditional std deviation forecast:")
print(vol_fc.values.round(4))
from scipy.stats import t as t_dist
import numpy as np
def diebold_mariano_test(
actual: np.ndarray,
forecast1: np.ndarray,
forecast2: np.ndarray,
h: int = 1,
loss: str = "mse",
) -> dict:
"""
Diebold-Mariano test for equal predictive accuracy.
H0: E[d_t] = 0 (equal forecast accuracy)
H1: model 1 is more accurate (one-sided) or different (two-sided)
Args:
actual: Array of true values.
forecast1: Array of model 1 predictions.
forecast2: Array of model 2 predictions.
h: Forecast horizon (>1 needs Newey-West HAC).
loss: 'mse' or 'mae'.
Returns:
dict with DM statistic, p-value, and conclusion.
"""
e1 = actual - forecast1
e2 = actual - forecast2
if loss == "mse":
d = e1**2 - e2**2
elif loss == "mae":
d = np.abs(e1) - np.abs(e2)
else:
raise ValueError("loss must be 'mse' or 'mae'")
n = len(d)
d_bar = np.mean(d)
# Newey-West HAC variance (needed when h > 1)
gamma0 = np.var(d, ddof=1)
gamma_sum = sum(
(1 - k / (h + 1)) * np.cov(d[k:], d[:-k])[0, 1]
for k in range(1, h)
) if h > 1 else 0.0
var_d = (gamma0 + 2 * gamma_sum) / n
se_d = np.sqrt(max(var_d, 1e-12))
dm_stat = d_bar / se_d
p_value = 2 * t_dist.sf(abs(dm_stat), df=n - 1) # two-sided
return {
"dm_statistic": float(dm_stat),
"p_value" : float(p_value),
"mean_diff" : float(d_bar),
"conclusion" : "Model 1 better" if dm_stat < 0 else "Model 2 better"
if p_value < 0.05 else "No significant difference",
}
# Example usage
np.random.seed(10)
y_true = np.random.randn(100)
fc1 = y_true + np.random.randn(100) * 0.8 # model 1: noisier
fc2 = y_true + np.random.randn(100) * 1.2 # model 2: even noisier
dm = diebold_mariano_test(y_true, fc1, fc2, h=1, loss="mse")
print(f"DM statistic : {dm['dm_statistic']:.4f}")
print(f"p-value : {dm['p_value']:.4f}")
print(f"Conclusion : {dm['conclusion']}")
| Error / Symptom | Cause | Fix |
|---|---|---|
MLE Optimization Failed to Converge | Poor starting values | Set start_params manually; try different method (e.g., 'powell') |
ARIMA ACF of residuals shows autocorrelation | Under-specified model | Increase p or q; check Ljung-Box test on residuals |
KPSS LevelWarning: p-value is outside look-up table | Extreme test statistic | Series is strongly non-stationary; apply differencing |
VAR: LinAlgError: Singular matrix | Collinear variables | Drop one collinear variable or apply first difference |
GARCH: alpha + beta >= 1 | Near-integrated volatility | Use IGARCH or constrain parameters: constraints={'type':'ineq','fun': lambda p: 0.999 - p[1] - p[2]} |
pmdarima auto_arima very slow | Stepwise=False searches all combos | Set stepwise=True; reduce max_p, max_q |
| Version | Date | Change |
|---|---|---|
| 1.0.0 | 2026-03-17 | Initial release — ARIMA, SARIMA, STL, VAR, GARCH, DM test |