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claude-scientific-skills/scientific-skills/timesfm-forecasting/examples/anomaly-detection/detect_anomalies.py
Clayton Young df58339850 feat(timesfm): complete all three examples with quality docs 
- anomaly-detection: full two-phase rewrite (context Z-score + forecast PI),
  2-panel viz, Sep 2023 correctly flagged CRITICAL (z=+3.03)
- covariates-forecasting: v3 rewrite with variable-shadowing bug fixed,
  2x2 shared-axis viz showing actionable covariate decomposition,
  108-row CSV with distinct per-store price arrays
- global-temperature: output/ subfolder reorganization (all 6 output files
  moved, 5 scripts + shell script paths updated)
- SKILL.md: added Examples table, Quality Checklist, Common Mistakes (8 items),
  Validation & Verification with regression assertions
- .gitattributes already at repo root covering all binary types
2026-02-23 07:43:04 -05:00

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#!/usr/bin/env python3
"""
TimesFM Anomaly Detection Example — Two-Phase Method
Phase 1 (context): Linear detrend + Z-score on 36 months of real NOAA
temperature anomaly data (2022-01 through 2024-12).
Sep 2023 (1.47 C) is a known critical outlier.
Phase 2 (forecast): TimesFM quantile prediction intervals on a 12-month
synthetic future with 3 injected anomalies.
Outputs:
output/anomaly_detection.png -- 2-panel visualization
output/anomaly_detection.json -- structured detection records
"""
from __future__ import annotations
import json
from pathlib import Path
import matplotlib
matplotlib.use("Agg")
import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
HORIZON = 12
DATA_FILE = (
Path(__file__).parent.parent / "global-temperature" / "temperature_anomaly.csv"
)
OUTPUT_DIR = Path(__file__).parent / "output"
CRITICAL_Z = 3.0
WARNING_Z = 2.0
# quant_fc index mapping: 0=mean, 1=q10, 2=q20, ..., 9=q90
IDX_Q10, IDX_Q20, IDX_Q80, IDX_Q90 = 1, 2, 8, 9
CLR = {"CRITICAL": "#e02020", "WARNING": "#f08030", "NORMAL": "#4a90d9"}
# ---------------------------------------------------------------------------
# Phase 1: context anomaly detection
# ---------------------------------------------------------------------------
def detect_context_anomalies(
values: np.ndarray,
dates: list,
) -> tuple[list[dict], np.ndarray, np.ndarray, float]:
"""Linear detrend + Z-score anomaly detection on context period.
Returns
-------
records : list of dicts, one per month
trend_line : fitted linear trend values (same length as values)
residuals : actual - trend_line
res_std : std of residuals (used as sigma for threshold bands)
"""
n = len(values)
idx = np.arange(n, dtype=float)
coeffs = np.polyfit(idx, values, 1)
trend_line = np.polyval(coeffs, idx)
residuals = values - trend_line
res_std = residuals.std()
records = []
for i, (d, v, r) in enumerate(zip(dates, values, residuals)):
z = r / res_std if res_std > 0 else 0.0
if abs(z) >= CRITICAL_Z:
severity = "CRITICAL"
elif abs(z) >= WARNING_Z:
severity = "WARNING"
else:
severity = "NORMAL"
records.append(
{
"date": str(d)[:7],
"value": round(float(v), 4),
"trend": round(float(trend_line[i]), 4),
"residual": round(float(r), 4),
"z_score": round(float(z), 3),
"severity": severity,
}
)
return records, trend_line, residuals, res_std
# ---------------------------------------------------------------------------
# Phase 2: synthetic future + forecast anomaly detection
# ---------------------------------------------------------------------------
def build_synthetic_future(
context: np.ndarray,
n: int,
seed: int = 42,
) -> tuple[np.ndarray, list[int]]:
"""Build a plausible future with 3 injected anomalies.
Injected months: 3, 8, 11 (0-indexed within the 12-month horizon).
Returns (future_values, injected_indices).
"""
rng = np.random.default_rng(seed)
trend = np.linspace(context[-6:].mean(), context[-6:].mean() + 0.05, n)
noise = rng.normal(0, 0.1, n)
future = trend + noise
injected = [3, 8, 11]
future[3] += 0.7 # CRITICAL spike
future[8] -= 0.65 # CRITICAL dip
future[11] += 0.45 # WARNING spike
return future.astype(np.float32), injected
def detect_forecast_anomalies(
future_values: np.ndarray,
point: np.ndarray,
quant_fc: np.ndarray,
future_dates: list,
injected_at: list[int],
) -> list[dict]:
"""Classify each forecast month by which PI band it falls outside.
CRITICAL = outside 80% PI (q10-q90)
WARNING = outside 60% PI (q20-q80) but inside 80% PI
NORMAL = inside 60% PI
"""
q10 = quant_fc[IDX_Q10]
q20 = quant_fc[IDX_Q20]
q80 = quant_fc[IDX_Q80]
q90 = quant_fc[IDX_Q90]
records = []
for i, (d, fv, pt) in enumerate(zip(future_dates, future_values, point)):
outside_80 = fv < q10[i] or fv > q90[i]
outside_60 = fv < q20[i] or fv > q80[i]
if outside_80:
severity = "CRITICAL"
elif outside_60:
severity = "WARNING"
else:
severity = "NORMAL"
records.append(
{
"date": str(d)[:7],
"actual": round(float(fv), 4),
"forecast": round(float(pt), 4),
"q10": round(float(q10[i]), 4),
"q20": round(float(q20[i]), 4),
"q80": round(float(q80[i]), 4),
"q90": round(float(q90[i]), 4),
"severity": severity,
"was_injected": i in injected_at,
}
)
return records
# ---------------------------------------------------------------------------
# Visualization
# ---------------------------------------------------------------------------
def plot_results(
context_dates: list,
context_values: np.ndarray,
ctx_records: list[dict],
trend_line: np.ndarray,
residuals: np.ndarray,
res_std: float,
future_dates: list,
future_values: np.ndarray,
point_fc: np.ndarray,
quant_fc: np.ndarray,
fc_records: list[dict],
) -> None:
OUTPUT_DIR.mkdir(exist_ok=True)
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(15, 10), gridspec_kw={"hspace": 0.42})
fig.suptitle(
"TimesFM Anomaly Detection — Two-Phase Method", fontsize=14, fontweight="bold"
)
# -----------------------------------------------------------------------
# Panel 1 — full timeline
# -----------------------------------------------------------------------
ctx_x = [pd.Timestamp(d) for d in context_dates]
fut_x = [pd.Timestamp(d) for d in future_dates]
divider = ctx_x[-1]
# context: blue line + trend + 2sigma band
ax1.plot(
ctx_x,
context_values,
color=CLR["NORMAL"],
lw=2,
marker="o",
ms=4,
label="Observed (context)",
)
ax1.plot(ctx_x, trend_line, color="#aaaaaa", lw=1.5, ls="--", label="Linear trend")
ax1.fill_between(
ctx_x,
trend_line - 2 * res_std,
trend_line + 2 * res_std,
alpha=0.15,
color=CLR["NORMAL"],
label="+/-2sigma band",
)
# context anomaly markers
seen_ctx: set[str] = set()
for rec in ctx_records:
if rec["severity"] == "NORMAL":
continue
d = pd.Timestamp(rec["date"])
v = rec["value"]
sev = rec["severity"]
lbl = f"Context {sev}" if sev not in seen_ctx else None
seen_ctx.add(sev)
ax1.scatter(d, v, marker="D", s=90, color=CLR[sev], zorder=6, label=lbl)
ax1.annotate(
f"z={rec['z_score']:+.1f}",
(d, v),
textcoords="offset points",
xytext=(0, 9),
fontsize=7.5,
ha="center",
color=CLR[sev],
)
# forecast section
q10 = quant_fc[IDX_Q10]
q20 = quant_fc[IDX_Q20]
q80 = quant_fc[IDX_Q80]
q90 = quant_fc[IDX_Q90]
ax1.plot(fut_x, future_values, "k--", lw=1.5, label="Synthetic future (truth)")
ax1.plot(
fut_x,
point_fc,
color=CLR["CRITICAL"],
lw=2,
marker="s",
ms=4,
label="TimesFM point forecast",
)
ax1.fill_between(fut_x, q10, q90, alpha=0.15, color=CLR["CRITICAL"], label="80% PI")
ax1.fill_between(fut_x, q20, q80, alpha=0.25, color=CLR["CRITICAL"], label="60% PI")
seen_fc: set[str] = set()
for i, rec in enumerate(fc_records):
if rec["severity"] == "NORMAL":
continue
d = pd.Timestamp(rec["date"])
v = rec["actual"]
sev = rec["severity"]
mk = "X" if sev == "CRITICAL" else "^"
lbl = f"Forecast {sev}" if sev not in seen_fc else None
seen_fc.add(sev)
ax1.scatter(d, v, marker=mk, s=100, color=CLR[sev], zorder=6, label=lbl)
ax1.axvline(divider, color="#555555", lw=1.5, ls=":")
ax1.text(
divider,
ax1.get_ylim()[1] if ax1.get_ylim()[1] != 0 else 1.5,
" <- Context | Forecast ->",
fontsize=8.5,
color="#555555",
style="italic",
va="top",
)
ax1.annotate(
"Context: D = Z-score anomaly | Forecast: X = CRITICAL, ^ = WARNING",
xy=(0.01, 0.04),
xycoords="axes fraction",
fontsize=8,
bbox=dict(boxstyle="round", fc="white", ec="#cccccc", alpha=0.9),
)
ax1.set_ylabel("Temperature Anomaly (C)", fontsize=10)
ax1.legend(ncol=2, fontsize=7.5, loc="upper left")
ax1.grid(True, alpha=0.22)
# -----------------------------------------------------------------------
# Panel 2 — deviation bars across all 48 months
# -----------------------------------------------------------------------
all_labels: list[str] = []
bar_colors: list[str] = []
bar_heights: list[float] = []
for rec in ctx_records:
all_labels.append(rec["date"])
bar_heights.append(rec["residual"])
bar_colors.append(CLR[rec["severity"]])
fc_deviations: list[float] = []
for rec in fc_records:
all_labels.append(rec["date"])
dev = rec["actual"] - rec["forecast"]
fc_deviations.append(dev)
bar_heights.append(dev)
bar_colors.append(CLR[rec["severity"]])
xs = np.arange(len(all_labels))
ax2.bar(xs[:36], bar_heights[:36], color=bar_colors[:36], alpha=0.8)
ax2.bar(xs[36:], bar_heights[36:], color=bar_colors[36:], alpha=0.8)
# threshold lines for context section only
ax2.hlines(
[2 * res_std, -2 * res_std], -0.5, 35.5, colors=CLR["NORMAL"], lw=1.2, ls="--"
)
ax2.hlines(
[3 * res_std, -3 * res_std], -0.5, 35.5, colors=CLR["NORMAL"], lw=1.0, ls=":"
)
# PI bands for forecast section
fc_xs = xs[36:]
ax2.fill_between(
fc_xs,
q10 - point_fc,
q90 - point_fc,
alpha=0.12,
color=CLR["CRITICAL"],
step="mid",
)
ax2.fill_between(
fc_xs,
q20 - point_fc,
q80 - point_fc,
alpha=0.20,
color=CLR["CRITICAL"],
step="mid",
)
ax2.axvline(35.5, color="#555555", lw=1.5, ls="--")
ax2.axhline(0, color="black", lw=0.8, alpha=0.6)
ax2.text(
10,
ax2.get_ylim()[0] * 0.85 if ax2.get_ylim()[0] < 0 else -0.05,
"<- Context: delta from linear trend",
fontsize=8,
style="italic",
color="#555555",
ha="center",
)
ax2.text(
41,
ax2.get_ylim()[0] * 0.85 if ax2.get_ylim()[0] < 0 else -0.05,
"Forecast: delta from TimesFM ->",
fontsize=8,
style="italic",
color="#555555",
ha="center",
)
tick_every = 3
ax2.set_xticks(xs[::tick_every])
ax2.set_xticklabels(all_labels[::tick_every], rotation=45, ha="right", fontsize=7)
ax2.set_ylabel("Delta from expected (C)", fontsize=10)
ax2.grid(True, alpha=0.22, axis="y")
legend_patches = [
mpatches.Patch(color=CLR["CRITICAL"], label="CRITICAL"),
mpatches.Patch(color=CLR["WARNING"], label="WARNING"),
mpatches.Patch(color=CLR["NORMAL"], label="Normal"),
]
ax2.legend(handles=legend_patches, fontsize=8, loc="upper right")
output_path = OUTPUT_DIR / "anomaly_detection.png"
plt.savefig(output_path, dpi=150, bbox_inches="tight")
plt.close()
print(f"\n Saved: {output_path}")
# ---------------------------------------------------------------------------
# Main
# ---------------------------------------------------------------------------
def main() -> None:
print("=" * 68)
print(" TIMESFM ANOMALY DETECTION — TWO-PHASE METHOD")
print("=" * 68)
# --- Load context data ---------------------------------------------------
df = pd.read_csv(DATA_FILE)
df["date"] = pd.to_datetime(df["date"])
df = df.sort_values("date").reset_index(drop=True)
context_values = df["anomaly_c"].values.astype(np.float32)
context_dates = [pd.Timestamp(d) for d in df["date"].tolist()]
start_str = context_dates[0].strftime('%Y-%m') if not pd.isnull(context_dates[0]) else '?'
end_str = context_dates[-1].strftime('%Y-%m') if not pd.isnull(context_dates[-1]) else '?'
print(f"\n Context: {len(context_values)} months ({start_str} - {end_str})")
# --- Phase 1: context anomaly detection ----------------------------------
ctx_records, trend_line, residuals, res_std = detect_context_anomalies(
context_values, context_dates
)
ctx_critical = [r for r in ctx_records if r["severity"] == "CRITICAL"]
ctx_warning = [r for r in ctx_records if r["severity"] == "WARNING"]
print(f"\n [Phase 1] Context anomalies (Z-score, sigma={res_std:.3f} C):")
print(f" CRITICAL (|Z|>={CRITICAL_Z}): {len(ctx_critical)}")
for r in ctx_critical:
print(f" {r['date']} {r['value']:+.3f} C z={r['z_score']:+.2f}")
print(f" WARNING (|Z|>={WARNING_Z}): {len(ctx_warning)}")
for r in ctx_warning:
print(f" {r['date']} {r['value']:+.3f} C z={r['z_score']:+.2f}")
# --- Load TimesFM --------------------------------------------------------
print("\n Loading TimesFM 1.0 ...")
import timesfm
hparams = timesfm.TimesFmHparams(horizon_len=HORIZON)
checkpoint = timesfm.TimesFmCheckpoint(
huggingface_repo_id="google/timesfm-1.0-200m-pytorch"
)
model = timesfm.TimesFm(hparams=hparams, checkpoint=checkpoint)
point_out, quant_out = model.forecast([context_values], freq=[0])
point_fc = point_out[0] # shape (HORIZON,)
quant_fc = quant_out[0].T # shape (10, HORIZON)
# --- Build synthetic future + Phase 2 detection --------------------------
future_values, injected = build_synthetic_future(context_values, HORIZON)
last_date = context_dates[-1]
future_dates = [last_date + pd.DateOffset(months=i + 1) for i in range(HORIZON)]
fc_records = detect_forecast_anomalies(
future_values, point_fc, quant_fc, future_dates, injected
)
fc_critical = [r for r in fc_records if r["severity"] == "CRITICAL"]
fc_warning = [r for r in fc_records if r["severity"] == "WARNING"]
print(f"\n [Phase 2] Forecast anomalies (quantile PI, horizon={HORIZON} months):")
print(f" CRITICAL (outside 80% PI): {len(fc_critical)}")
for r in fc_critical:
print(
f" {r['date']} actual={r['actual']:+.3f} "
f"fc={r['forecast']:+.3f} injected={r['was_injected']}"
)
print(f" WARNING (outside 60% PI): {len(fc_warning)}")
for r in fc_warning:
print(
f" {r['date']} actual={r['actual']:+.3f} "
f"fc={r['forecast']:+.3f} injected={r['was_injected']}"
)
# --- Plot ----------------------------------------------------------------
print("\n Generating 2-panel visualization...")
plot_results(
context_dates,
context_values,
ctx_records,
trend_line,
residuals,
res_std,
future_dates,
future_values,
point_fc,
quant_fc,
fc_records,
)
# --- Save JSON -----------------------------------------------------------
OUTPUT_DIR.mkdir(exist_ok=True)
out = {
"method": "two_phase",
"context_method": "linear_detrend_zscore",
"forecast_method": "quantile_prediction_intervals",
"thresholds": {
"critical_z": CRITICAL_Z,
"warning_z": WARNING_Z,
"pi_critical_pct": 80,
"pi_warning_pct": 60,
},
"context_summary": {
"total": len(ctx_records),
"critical": len(ctx_critical),
"warning": len(ctx_warning),
"normal": len([r for r in ctx_records if r["severity"] == "NORMAL"]),
"res_std": round(float(res_std), 5),
},
"forecast_summary": {
"total": len(fc_records),
"critical": len(fc_critical),
"warning": len(fc_warning),
"normal": len([r for r in fc_records if r["severity"] == "NORMAL"]),
},
"context_detections": ctx_records,
"forecast_detections": fc_records,
}
json_path = OUTPUT_DIR / "anomaly_detection.json"
with open(json_path, "w") as f:
json.dump(out, f, indent=2)
print(f" Saved: {json_path}")
print("\n" + "=" * 68)
print(" SUMMARY")
print("=" * 68)
print(
f" Context ({len(ctx_records)} months): "
f"{len(ctx_critical)} CRITICAL, {len(ctx_warning)} WARNING"
)
print(
f" Forecast ({len(fc_records)} months): "
f"{len(fc_critical)} CRITICAL, {len(fc_warning)} WARNING"
)
print("=" * 68)
if __name__ == "__main__":
main()
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