pdm_forecast
Read-only
Forecast a series trend and compute ETA to warn/alarm limits, with degradation pattern and remaining useful life for predictive maintenance.
Instructions
[READ][risk=low] Forecast a value's trend + time until it crosses a warn/alarm limit.
The predictive step above baseline_check (which flags a violation that already happened): fits a
robust Theil-Sen trend to the recent history and, if it continues, estimates the ETA to the
nearest limit in the direction of travel — the early warning that makes maintenance predictive
(inverter/turbine degradation, bearing drift, filter clogging). Refuses thin history; read-only,
pure over the provided series; no device I/O.
Beyond the trend, the result deepens into three explainable, stdlib-only views: a degradation
'pattern' (gradual vs sudden vs cyclic), a remaining-useful-life 'rul' block when degrading
(linear + exponential extrapolation to the limit, a confidence band from the slope spread, and a
fit R^2), and optional time-domain 'waveform' features (RMS/kurtosis/crest/... for
vibration-type signals). Each states its own uncertainty rather than guessing.
Args:
series: Time-ordered samples: [{value, timestamp?}] (timestamp ISO-8601; if all present the
ETA is in seconds, otherwise in samples). >= 30 numeric samples required.
warn_high/alarm_high/warn_low/alarm_low: Optional limits; the forecast targets the nearest
one in the trend's direction (rising → highs, falling → lows).
imminent_within_s: ETA (seconds) at/under which status is 'imminent' (default 86400 = 24h).
include_waveform: Add the time-domain 'waveform' feature block (default True). Set False for
slow trend-only signals where vibration features do not apply.
Returns dict: {status (insufficient_data|stable|degrading|imminent), samples, direction,
slope_per_unit, unit (s|samples), current, limit:{name,value}, eta_to_limit,
degradation:{pattern,confidence,rationale,metrics},
waveform:{rms,crest_factor,kurtosis,...} (when include_waveform),
rul:{linear,exponential,eta_band,recommended_model,confidence,...} (when degrading)}.
Example: pdm_forecast(series=[{"value": 62.1, "timestamp": "2026-07-12T00:00:00Z"}, ...],
warn_high=75, alarm_high=85).
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| series | Yes | ||
| warn_low | No | ||
| alarm_low | No | ||
| warn_high | No | ||
| alarm_high | No | ||
| include_waveform | No | ||
| imminent_within_s | No |