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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
This commit is contained in:
Clayton Young
@@ -1,20 +1,17 @@
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#!/usr/bin/env python3
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"""
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TimesFM Anomaly Detection Example
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TimesFM Anomaly Detection Example — Two-Phase Method
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Demonstrates using TimesFM quantile forecasts as prediction intervals
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for anomaly detection. Approach:
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1. Use 36 months of real data as context
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2. Create synthetic 12-month future (natural continuation of trend)
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3. Inject 3 clear anomalies into that future
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4. Forecast with quantile intervals → flag anomalies by severity
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Phase 1 (context): Linear detrend + Z-score on 36 months of real NOAA
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temperature anomaly data (2022-01 through 2024-12).
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Sep 2023 (1.47 C) is a known critical outlier.
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TimesFM has NO built-in anomaly detection. Quantile forecasts provide
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natural prediction intervals — values outside them are statistically unusual.
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Phase 2 (forecast): TimesFM quantile prediction intervals on a 12-month
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synthetic future with 3 injected anomalies.
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Quantile index reference (index 0 = mean, 1-9 = q10-q90):
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80% PI = q10 (idx 1) to q90 (idx 9)
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60% PI = q20 (idx 2) to q80 (idx 8)
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Outputs:
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output/anomaly_detection.png -- 2-panel visualization
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output/anomaly_detection.json -- structured detection records
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"""
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from __future__ import annotations
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@@ -22,272 +19,505 @@ from __future__ import annotations
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import json
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from pathlib import Path
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import matplotlib
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matplotlib.use("Agg")
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import matplotlib.patches as mpatches
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import matplotlib.pyplot as plt
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import matplotlib.dates as mdates
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import numpy as np
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import pandas as pd
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import timesfm
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# Configuration
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HORIZON = 12 # Forecast horizon (months)
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HORIZON = 12
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DATA_FILE = (
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Path(__file__).parent.parent / "global-temperature" / "temperature_anomaly.csv"
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)
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OUTPUT_DIR = Path(__file__).parent / "output"
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# Anomaly thresholds using available quantile outputs
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# 80% PI = q10-q90 → "critical" if outside
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# 60% PI = q20-q80 → "warning" if outside
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CRITICAL_Z = 3.0
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WARNING_Z = 2.0
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# quant_fc index mapping: 0=mean, 1=q10, 2=q20, ..., 9=q90
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IDX_Q10, IDX_Q20, IDX_Q80, IDX_Q90 = 1, 2, 8, 9
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CLR = {"CRITICAL": "#e02020", "WARNING": "#f08030", "NORMAL": "#4a90d9"}
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# ---------------------------------------------------------------------------
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# Phase 1: context anomaly detection
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# ---------------------------------------------------------------------------
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def detect_context_anomalies(
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values: np.ndarray,
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dates: list,
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) -> tuple[list[dict], np.ndarray, np.ndarray, float]:
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"""Linear detrend + Z-score anomaly detection on context period.
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Returns
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-------
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records : list of dicts, one per month
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trend_line : fitted linear trend values (same length as values)
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residuals : actual - trend_line
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res_std : std of residuals (used as sigma for threshold bands)
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"""
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n = len(values)
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idx = np.arange(n, dtype=float)
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coeffs = np.polyfit(idx, values, 1)
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trend_line = np.polyval(coeffs, idx)
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residuals = values - trend_line
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res_std = residuals.std()
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records = []
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for i, (d, v, r) in enumerate(zip(dates, values, residuals)):
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z = r / res_std if res_std > 0 else 0.0
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if abs(z) >= CRITICAL_Z:
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severity = "CRITICAL"
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elif abs(z) >= WARNING_Z:
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severity = "WARNING"
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else:
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severity = "NORMAL"
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records.append(
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{
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"date": str(d)[:7],
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"value": round(float(v), 4),
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"trend": round(float(trend_line[i]), 4),
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"residual": round(float(r), 4),
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"z_score": round(float(z), 3),
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"severity": severity,
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}
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)
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return records, trend_line, residuals, res_std
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# ---------------------------------------------------------------------------
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# Phase 2: synthetic future + forecast anomaly detection
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# ---------------------------------------------------------------------------
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def build_synthetic_future(
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context: np.ndarray, n: int, seed: int = 42
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context: np.ndarray,
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n: int,
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seed: int = 42,
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) -> tuple[np.ndarray, list[int]]:
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"""Build synthetic future that looks like a natural continuation.
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"""Build a plausible future with 3 injected anomalies.
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Takes the mean/std of the last 6 context months as the baseline,
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then injects 3 clear anomalies (2 high, 1 low) at fixed positions.
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Injected months: 3, 8, 11 (0-indexed within the 12-month horizon).
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Returns (future_values, injected_indices).
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"""
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rng = np.random.default_rng(seed)
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recent_mean = float(context[-6:].mean())
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recent_std = float(context[-6:].std())
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trend = np.linspace(context[-6:].mean(), context[-6:].mean() + 0.05, n)
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noise = rng.normal(0, 0.1, n)
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future = trend + noise
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# Natural-looking continuation: small gaussian noise around recent mean
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future = recent_mean + rng.normal(0, recent_std * 0.4, n).astype(np.float32)
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injected = [3, 8, 11]
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future[3] += 0.7 # CRITICAL spike
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future[8] -= 0.65 # CRITICAL dip
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future[11] += 0.45 # WARNING spike
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# Inject 3 unmistakable anomalies
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anomaly_cfg = [
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(2, +0.55), # month 3 — large spike up
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(7, -0.50), # month 8 — large dip down
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(10, +0.48), # month 11 — spike up
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return future.astype(np.float32), injected
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def detect_forecast_anomalies(
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future_values: np.ndarray,
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point: np.ndarray,
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quant_fc: np.ndarray,
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future_dates: list,
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injected_at: list[int],
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) -> list[dict]:
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"""Classify each forecast month by which PI band it falls outside.
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CRITICAL = outside 80% PI (q10-q90)
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WARNING = outside 60% PI (q20-q80) but inside 80% PI
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NORMAL = inside 60% PI
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"""
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q10 = quant_fc[IDX_Q10]
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q20 = quant_fc[IDX_Q20]
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q80 = quant_fc[IDX_Q80]
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q90 = quant_fc[IDX_Q90]
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records = []
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for i, (d, fv, pt) in enumerate(zip(future_dates, future_values, point)):
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outside_80 = fv < q10[i] or fv > q90[i]
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outside_60 = fv < q20[i] or fv > q80[i]
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if outside_80:
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severity = "CRITICAL"
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elif outside_60:
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severity = "WARNING"
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else:
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severity = "NORMAL"
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records.append(
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{
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"date": str(d)[:7],
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"actual": round(float(fv), 4),
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"forecast": round(float(pt), 4),
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"q10": round(float(q10[i]), 4),
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"q20": round(float(q20[i]), 4),
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"q80": round(float(q80[i]), 4),
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"q90": round(float(q90[i]), 4),
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"severity": severity,
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"was_injected": i in injected_at,
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}
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)
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return records
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# ---------------------------------------------------------------------------
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# Visualization
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# ---------------------------------------------------------------------------
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def plot_results(
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context_dates: list,
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context_values: np.ndarray,
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ctx_records: list[dict],
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trend_line: np.ndarray,
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residuals: np.ndarray,
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res_std: float,
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future_dates: list,
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future_values: np.ndarray,
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point_fc: np.ndarray,
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quant_fc: np.ndarray,
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fc_records: list[dict],
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) -> None:
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OUTPUT_DIR.mkdir(exist_ok=True)
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fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(15, 10), gridspec_kw={"hspace": 0.42})
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fig.suptitle(
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"TimesFM Anomaly Detection — Two-Phase Method", fontsize=14, fontweight="bold"
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)
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# -----------------------------------------------------------------------
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# Panel 1 — full timeline
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# -----------------------------------------------------------------------
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ctx_x = [pd.Timestamp(d) for d in context_dates]
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fut_x = [pd.Timestamp(d) for d in future_dates]
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divider = ctx_x[-1]
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# context: blue line + trend + 2sigma band
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ax1.plot(
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ctx_x,
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context_values,
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color=CLR["NORMAL"],
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lw=2,
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marker="o",
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ms=4,
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label="Observed (context)",
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)
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ax1.plot(ctx_x, trend_line, color="#aaaaaa", lw=1.5, ls="--", label="Linear trend")
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ax1.fill_between(
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ctx_x,
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trend_line - 2 * res_std,
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trend_line + 2 * res_std,
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alpha=0.15,
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color=CLR["NORMAL"],
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label="+/-2sigma band",
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)
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# context anomaly markers
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seen_ctx: set[str] = set()
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for rec in ctx_records:
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if rec["severity"] == "NORMAL":
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continue
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d = pd.Timestamp(rec["date"])
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v = rec["value"]
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sev = rec["severity"]
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lbl = f"Context {sev}" if sev not in seen_ctx else None
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seen_ctx.add(sev)
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ax1.scatter(d, v, marker="D", s=90, color=CLR[sev], zorder=6, label=lbl)
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ax1.annotate(
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f"z={rec['z_score']:+.1f}",
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(d, v),
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textcoords="offset points",
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xytext=(0, 9),
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fontsize=7.5,
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ha="center",
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color=CLR[sev],
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)
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# forecast section
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q10 = quant_fc[IDX_Q10]
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q20 = quant_fc[IDX_Q20]
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q80 = quant_fc[IDX_Q80]
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q90 = quant_fc[IDX_Q90]
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ax1.plot(fut_x, future_values, "k--", lw=1.5, label="Synthetic future (truth)")
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ax1.plot(
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fut_x,
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point_fc,
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color=CLR["CRITICAL"],
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lw=2,
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marker="s",
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ms=4,
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label="TimesFM point forecast",
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)
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ax1.fill_between(fut_x, q10, q90, alpha=0.15, color=CLR["CRITICAL"], label="80% PI")
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ax1.fill_between(fut_x, q20, q80, alpha=0.25, color=CLR["CRITICAL"], label="60% PI")
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seen_fc: set[str] = set()
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for i, rec in enumerate(fc_records):
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if rec["severity"] == "NORMAL":
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continue
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d = pd.Timestamp(rec["date"])
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v = rec["actual"]
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sev = rec["severity"]
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mk = "X" if sev == "CRITICAL" else "^"
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lbl = f"Forecast {sev}" if sev not in seen_fc else None
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seen_fc.add(sev)
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ax1.scatter(d, v, marker=mk, s=100, color=CLR[sev], zorder=6, label=lbl)
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ax1.axvline(divider, color="#555555", lw=1.5, ls=":")
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ax1.text(
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divider,
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ax1.get_ylim()[1] if ax1.get_ylim()[1] != 0 else 1.5,
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" <- Context | Forecast ->",
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fontsize=8.5,
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color="#555555",
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style="italic",
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va="top",
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)
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ax1.annotate(
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"Context: D = Z-score anomaly | Forecast: X = CRITICAL, ^ = WARNING",
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xy=(0.01, 0.04),
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xycoords="axes fraction",
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fontsize=8,
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bbox=dict(boxstyle="round", fc="white", ec="#cccccc", alpha=0.9),
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)
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ax1.set_ylabel("Temperature Anomaly (C)", fontsize=10)
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ax1.legend(ncol=2, fontsize=7.5, loc="upper left")
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ax1.grid(True, alpha=0.22)
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# -----------------------------------------------------------------------
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# Panel 2 — deviation bars across all 48 months
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# -----------------------------------------------------------------------
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all_labels: list[str] = []
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bar_colors: list[str] = []
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bar_heights: list[float] = []
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for rec in ctx_records:
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all_labels.append(rec["date"])
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bar_heights.append(rec["residual"])
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bar_colors.append(CLR[rec["severity"]])
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fc_deviations: list[float] = []
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for rec in fc_records:
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all_labels.append(rec["date"])
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dev = rec["actual"] - rec["forecast"]
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fc_deviations.append(dev)
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bar_heights.append(dev)
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bar_colors.append(CLR[rec["severity"]])
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xs = np.arange(len(all_labels))
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ax2.bar(xs[:36], bar_heights[:36], color=bar_colors[:36], alpha=0.8)
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ax2.bar(xs[36:], bar_heights[36:], color=bar_colors[36:], alpha=0.8)
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# threshold lines for context section only
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ax2.hlines(
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[2 * res_std, -2 * res_std], -0.5, 35.5, colors=CLR["NORMAL"], lw=1.2, ls="--"
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)
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ax2.hlines(
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[3 * res_std, -3 * res_std], -0.5, 35.5, colors=CLR["NORMAL"], lw=1.0, ls=":"
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)
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# PI bands for forecast section
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fc_xs = xs[36:]
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ax2.fill_between(
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fc_xs,
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q10 - point_fc,
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q90 - point_fc,
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alpha=0.12,
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color=CLR["CRITICAL"],
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step="mid",
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)
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ax2.fill_between(
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fc_xs,
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q20 - point_fc,
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q80 - point_fc,
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alpha=0.20,
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color=CLR["CRITICAL"],
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step="mid",
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)
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ax2.axvline(35.5, color="#555555", lw=1.5, ls="--")
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ax2.axhline(0, color="black", lw=0.8, alpha=0.6)
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ax2.text(
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10,
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ax2.get_ylim()[0] * 0.85 if ax2.get_ylim()[0] < 0 else -0.05,
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"<- Context: delta from linear trend",
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fontsize=8,
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style="italic",
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color="#555555",
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ha="center",
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)
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ax2.text(
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41,
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ax2.get_ylim()[0] * 0.85 if ax2.get_ylim()[0] < 0 else -0.05,
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"Forecast: delta from TimesFM ->",
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fontsize=8,
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style="italic",
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color="#555555",
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ha="center",
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)
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tick_every = 3
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ax2.set_xticks(xs[::tick_every])
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ax2.set_xticklabels(all_labels[::tick_every], rotation=45, ha="right", fontsize=7)
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ax2.set_ylabel("Delta from expected (C)", fontsize=10)
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ax2.grid(True, alpha=0.22, axis="y")
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legend_patches = [
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mpatches.Patch(color=CLR["CRITICAL"], label="CRITICAL"),
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mpatches.Patch(color=CLR["WARNING"], label="WARNING"),
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mpatches.Patch(color=CLR["NORMAL"], label="Normal"),
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]
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anomaly_indices = []
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for idx, delta in anomaly_cfg:
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future[idx] = recent_mean + delta
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anomaly_indices.append(idx)
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ax2.legend(handles=legend_patches, fontsize=8, loc="upper right")
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return future, sorted(anomaly_indices)
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output_path = OUTPUT_DIR / "anomaly_detection.png"
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plt.savefig(output_path, dpi=150, bbox_inches="tight")
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plt.close()
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print(f"\n Saved: {output_path}")
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# ---------------------------------------------------------------------------
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# Main
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# ---------------------------------------------------------------------------
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def main() -> None:
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print("=" * 60)
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print(" TIMESFM ANOMALY DETECTION DEMO")
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print("=" * 60)
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print("=" * 68)
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print(" TIMESFM ANOMALY DETECTION — TWO-PHASE METHOD")
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print("=" * 68)
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OUTPUT_DIR.mkdir(exist_ok=True)
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# ── Load all 36 months as context ─────────────────────────────
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print("\n📊 Loading temperature data (all 36 months as context)...")
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df = pd.read_csv(DATA_FILE, parse_dates=["date"])
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# --- Load context data ---------------------------------------------------
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df = pd.read_csv(DATA_FILE)
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df["date"] = pd.to_datetime(df["date"])
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df = df.sort_values("date").reset_index(drop=True)
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context_values = df["anomaly_c"].values.astype(np.float32) # all 36 months
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context_dates = df["date"].tolist()
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print(
|
||||
f" Context: {len(context_values)} months ({context_dates[0].strftime('%Y-%m')} → {context_dates[-1].strftime('%Y-%m')})"
|
||||
)
|
||||
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})")
|
||||
|
||||
# ── Build synthetic future with known anomalies ────────────────
|
||||
print("\n🔬 Building synthetic 12-month future with injected anomalies...")
|
||||
future_values, injected_at = build_synthetic_future(context_values, HORIZON)
|
||||
future_dates = pd.date_range(
|
||||
start=context_dates[-1] + pd.DateOffset(months=1),
|
||||
periods=HORIZON,
|
||||
freq="MS",
|
||||
)
|
||||
print(
|
||||
f" Anomalies injected at months: {[future_dates[i].strftime('%Y-%m') for i in injected_at]}"
|
||||
# --- 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
|
||||
|
||||
# ── Load TimesFM and forecast ──────────────────────────────────
|
||||
print("\n🤖 Loading TimesFM 1.0 (200M) PyTorch...")
|
||||
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)
|
||||
|
||||
print("\n📈 Forecasting...")
|
||||
point_fc, quant_fc = model.forecast([context_values], freq=[0])
|
||||
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)
|
||||
|
||||
# quantile_forecast shape: (1, horizon, 10)
|
||||
# index 0 = mean, index 1 = q10, ..., index 9 = q90
|
||||
point = point_fc[0] # shape (12,)
|
||||
q10 = quant_fc[0, :, IDX_Q10] # 10th pct
|
||||
q20 = quant_fc[0, :, IDX_Q20] # 20th pct
|
||||
q80 = quant_fc[0, :, IDX_Q80] # 80th pct
|
||||
q90 = quant_fc[0, :, IDX_Q90] # 90th pct
|
||||
# --- 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)]
|
||||
|
||||
print(f" Forecast mean: {point.mean():.3f}°C")
|
||||
print(f" 80% PI width: {(q90 - q10).mean():.3f}°C (avg)")
|
||||
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"]
|
||||
|
||||
# ── Detect anomalies ───────────────────────────────────────────
|
||||
print("\n🔍 Detecting anomalies...")
|
||||
records = []
|
||||
for i, (actual, fcast, lo60, hi60, lo80, hi80) in enumerate(
|
||||
zip(future_values, point, q20, q80, q10, q90)
|
||||
):
|
||||
month = future_dates[i].strftime("%Y-%m")
|
||||
|
||||
if actual < lo80 or actual > hi80:
|
||||
severity = "CRITICAL" # outside 80% PI
|
||||
elif actual < lo60 or actual > hi60:
|
||||
severity = "WARNING" # outside 60% PI
|
||||
else:
|
||||
severity = "NORMAL"
|
||||
|
||||
records.append(
|
||||
{
|
||||
"month": month,
|
||||
"actual": round(float(actual), 4),
|
||||
"forecast": round(float(fcast), 4),
|
||||
"lower_60pi": round(float(lo60), 4),
|
||||
"upper_60pi": round(float(hi60), 4),
|
||||
"lower_80pi": round(float(lo80), 4),
|
||||
"upper_80pi": round(float(hi80), 4),
|
||||
"severity": severity,
|
||||
"injected": (i in injected_at),
|
||||
}
|
||||
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']}"
|
||||
)
|
||||
|
||||
if severity != "NORMAL":
|
||||
dev = actual - fcast
|
||||
print(
|
||||
f" [{severity}] {month}: actual={actual:.2f} forecast={fcast:.2f} Δ={dev:+.2f}°C"
|
||||
)
|
||||
|
||||
# ── Visualise ─────────────────────────────────────────────────
|
||||
print("\n📊 Creating visualization...")
|
||||
|
||||
fig, axes = plt.subplots(2, 1, figsize=(13, 9))
|
||||
|
||||
clr = {"CRITICAL": "red", "WARNING": "orange", "NORMAL": "steelblue"}
|
||||
|
||||
# — Panel 1: full series ———————————————————————————————————————
|
||||
ax = axes[0]
|
||||
ax.plot(
|
||||
# --- Plot ----------------------------------------------------------------
|
||||
print("\n Generating 2-panel visualization...")
|
||||
plot_results(
|
||||
context_dates,
|
||||
context_values,
|
||||
"b-",
|
||||
lw=2,
|
||||
marker="o",
|
||||
ms=4,
|
||||
label="Context (36 months)",
|
||||
)
|
||||
ax.fill_between(
|
||||
future_dates, q10, q90, alpha=0.18, color="tomato", label="80% PI (q10–q90)"
|
||||
)
|
||||
ax.fill_between(
|
||||
future_dates, q20, q80, alpha=0.28, color="tomato", label="60% PI (q20–q80)"
|
||||
)
|
||||
ax.plot(future_dates, point, "r-", lw=2, marker="s", ms=5, label="Forecast")
|
||||
ax.plot(
|
||||
ctx_records,
|
||||
trend_line,
|
||||
residuals,
|
||||
res_std,
|
||||
future_dates,
|
||||
future_values,
|
||||
"k--",
|
||||
lw=1.3,
|
||||
alpha=0.5,
|
||||
label="Synthetic future (clean)",
|
||||
point_fc,
|
||||
quant_fc,
|
||||
fc_records,
|
||||
)
|
||||
|
||||
# mark anomalies
|
||||
for rec in records:
|
||||
if rec["severity"] != "NORMAL":
|
||||
dt = pd.to_datetime(rec["month"])
|
||||
c = "red" if rec["severity"] == "CRITICAL" else "orange"
|
||||
mk = "X" if rec["severity"] == "CRITICAL" else "^"
|
||||
ax.scatter(
|
||||
[dt], [rec["actual"]], c=c, s=220, marker=mk, zorder=6, linewidths=2
|
||||
)
|
||||
|
||||
ax.xaxis.set_major_formatter(mdates.DateFormatter("%Y-%m"))
|
||||
ax.xaxis.set_major_locator(mdates.MonthLocator(interval=3))
|
||||
plt.setp(ax.xaxis.get_majorticklabels(), rotation=45, ha="right")
|
||||
ax.set_ylabel("Temperature Anomaly (°C)", fontsize=11)
|
||||
ax.set_title(
|
||||
"TimesFM Anomaly Detection — Prediction Interval Method",
|
||||
fontsize=13,
|
||||
fontweight="bold",
|
||||
)
|
||||
ax.legend(loc="upper left", fontsize=9, ncol=2)
|
||||
ax.grid(True, alpha=0.25)
|
||||
ax.annotate(
|
||||
"X = Critical (outside 80% PI)\n▲ = Warning (outside 60% PI)",
|
||||
xy=(0.98, 0.04),
|
||||
xycoords="axes fraction",
|
||||
ha="right",
|
||||
fontsize=9,
|
||||
bbox=dict(boxstyle="round", facecolor="wheat", alpha=0.8),
|
||||
)
|
||||
|
||||
# — Panel 2: deviation bars ———————————————————————————————————
|
||||
ax2 = axes[1]
|
||||
deviations = future_values - point
|
||||
lo80_dev = q10 - point
|
||||
hi80_dev = q90 - point
|
||||
lo60_dev = q20 - point
|
||||
hi60_dev = q80 - point
|
||||
x = np.arange(HORIZON)
|
||||
|
||||
ax2.fill_between(x, lo80_dev, hi80_dev, alpha=0.15, color="tomato", label="80% PI")
|
||||
ax2.fill_between(x, lo60_dev, hi60_dev, alpha=0.25, color="tomato", label="60% PI")
|
||||
bar_colors = [clr[r["severity"]] for r in records]
|
||||
ax2.bar(x, deviations, color=bar_colors, alpha=0.75, edgecolor="black", lw=0.5)
|
||||
ax2.axhline(0, color="black", lw=1)
|
||||
|
||||
ax2.set_xticks(x)
|
||||
ax2.set_xticklabels(
|
||||
[r["month"] for r in records], rotation=45, ha="right", fontsize=9
|
||||
)
|
||||
ax2.set_ylabel("Δ from Forecast (°C)", fontsize=11)
|
||||
ax2.set_title(
|
||||
"Deviation from Forecast with Anomaly Thresholds",
|
||||
fontsize=13,
|
||||
fontweight="bold",
|
||||
)
|
||||
ax2.legend(loc="upper right", fontsize=9)
|
||||
ax2.grid(True, alpha=0.25, axis="y")
|
||||
|
||||
plt.tight_layout()
|
||||
png_path = OUTPUT_DIR / "anomaly_detection.png"
|
||||
plt.savefig(png_path, dpi=150, bbox_inches="tight")
|
||||
plt.close()
|
||||
print(f" Saved: {png_path}")
|
||||
|
||||
# ── Save JSON results ──────────────────────────────────────────
|
||||
summary = {
|
||||
"total": len(records),
|
||||
"critical": sum(1 for r in records if r["severity"] == "CRITICAL"),
|
||||
"warning": sum(1 for r in records if r["severity"] == "WARNING"),
|
||||
"normal": sum(1 for r in records if r["severity"] == "NORMAL"),
|
||||
}
|
||||
# --- Save JSON -----------------------------------------------------------
|
||||
OUTPUT_DIR.mkdir(exist_ok=True)
|
||||
out = {
|
||||
"method": "quantile_prediction_intervals",
|
||||
"description": (
|
||||
"Anomaly detection via TimesFM quantile forecasts. "
|
||||
"80% PI = q10–q90 (CRITICAL if violated). "
|
||||
"60% PI = q20–q80 (WARNING if violated)."
|
||||
),
|
||||
"context": "36 months of real NOAA temperature anomaly data (2022-2024)",
|
||||
"future": "12 synthetic months with 3 injected anomalies",
|
||||
"quantile_indices": {"q10": 1, "q20": 2, "q80": 8, "q90": 9},
|
||||
"summary": summary,
|
||||
"detections": records,
|
||||
"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(f" Saved: {json_path}")
|
||||
|
||||
# ── Summary ────────────────────────────────────────────────────
|
||||
print("\n" + "=" * 60)
|
||||
print(" ✅ ANOMALY DETECTION COMPLETE")
|
||||
print("=" * 60)
|
||||
print(f"\n Total future points : {summary['total']}")
|
||||
print(f" Critical (80% PI) : {summary['critical']}")
|
||||
print(f" Warning (60% PI) : {summary['warning']}")
|
||||
print(f" Normal : {summary['normal']}")
|
||||
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__":
|
||||
|
||||
@@ -1,152 +1,448 @@
|
||||
{
|
||||
"method": "quantile_prediction_intervals",
|
||||
"description": "Anomaly detection via TimesFM quantile forecasts. 80% PI = q10\u2013q90 (CRITICAL if violated). 60% PI = q20\u2013q80 (WARNING if violated).",
|
||||
"context": "36 months of real NOAA temperature anomaly data (2022-2024)",
|
||||
"future": "12 synthetic months with 3 injected anomalies",
|
||||
"quantile_indices": {
|
||||
"q10": 1,
|
||||
"q20": 2,
|
||||
"q80": 8,
|
||||
"q90": 9
|
||||
"method": "two_phase",
|
||||
"context_method": "linear_detrend_zscore",
|
||||
"forecast_method": "quantile_prediction_intervals",
|
||||
"thresholds": {
|
||||
"critical_z": 3.0,
|
||||
"warning_z": 2.0,
|
||||
"pi_critical_pct": 80,
|
||||
"pi_warning_pct": 60
|
||||
},
|
||||
"summary": {
|
||||
"context_summary": {
|
||||
"total": 36,
|
||||
"critical": 1,
|
||||
"warning": 0,
|
||||
"normal": 35,
|
||||
"res_std": 0.11362
|
||||
},
|
||||
"forecast_summary": {
|
||||
"total": 12,
|
||||
"critical": 3,
|
||||
"critical": 4,
|
||||
"warning": 1,
|
||||
"normal": 8
|
||||
"normal": 7
|
||||
},
|
||||
"detections": [
|
||||
"context_detections": [
|
||||
{
|
||||
"month": "2025-01",
|
||||
"actual": 1.2559,
|
||||
"date": "2022-01",
|
||||
"value": 0.89,
|
||||
"trend": 0.837,
|
||||
"residual": 0.053,
|
||||
"z_score": 0.467,
|
||||
"severity": "NORMAL"
|
||||
},
|
||||
{
|
||||
"date": "2022-02",
|
||||
"value": 0.89,
|
||||
"trend": 0.8514,
|
||||
"residual": 0.0386,
|
||||
"z_score": 0.34,
|
||||
"severity": "NORMAL"
|
||||
},
|
||||
{
|
||||
"date": "2022-03",
|
||||
"value": 1.02,
|
||||
"trend": 0.8658,
|
||||
"residual": 0.1542,
|
||||
"z_score": 1.357,
|
||||
"severity": "NORMAL"
|
||||
},
|
||||
{
|
||||
"date": "2022-04",
|
||||
"value": 0.88,
|
||||
"trend": 0.8803,
|
||||
"residual": -0.0003,
|
||||
"z_score": -0.002,
|
||||
"severity": "NORMAL"
|
||||
},
|
||||
{
|
||||
"date": "2022-05",
|
||||
"value": 0.85,
|
||||
"trend": 0.8947,
|
||||
"residual": -0.0447,
|
||||
"z_score": -0.394,
|
||||
"severity": "NORMAL"
|
||||
},
|
||||
{
|
||||
"date": "2022-06",
|
||||
"value": 0.88,
|
||||
"trend": 0.9092,
|
||||
"residual": -0.0292,
|
||||
"z_score": -0.257,
|
||||
"severity": "NORMAL"
|
||||
},
|
||||
{
|
||||
"date": "2022-07",
|
||||
"value": 0.88,
|
||||
"trend": 0.9236,
|
||||
"residual": -0.0436,
|
||||
"z_score": -0.384,
|
||||
"severity": "NORMAL"
|
||||
},
|
||||
{
|
||||
"date": "2022-08",
|
||||
"value": 0.9,
|
||||
"trend": 0.9381,
|
||||
"residual": -0.0381,
|
||||
"z_score": -0.335,
|
||||
"severity": "NORMAL"
|
||||
},
|
||||
{
|
||||
"date": "2022-09",
|
||||
"value": 0.88,
|
||||
"trend": 0.9525,
|
||||
"residual": -0.0725,
|
||||
"z_score": -0.638,
|
||||
"severity": "NORMAL"
|
||||
},
|
||||
{
|
||||
"date": "2022-10",
|
||||
"value": 0.95,
|
||||
"trend": 0.9669,
|
||||
"residual": -0.0169,
|
||||
"z_score": -0.149,
|
||||
"severity": "NORMAL"
|
||||
},
|
||||
{
|
||||
"date": "2022-11",
|
||||
"value": 0.77,
|
||||
"trend": 0.9814,
|
||||
"residual": -0.2114,
|
||||
"z_score": -1.86,
|
||||
"severity": "NORMAL"
|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
Binary file not shown.
|
Before Width: | Height: | Size: 193 KiB After Width: | Height: | Size: 212 KiB |
Reference in New Issue
Block a user