Electric Barometer · Metrics (eb-metrics)

Asymmetric, readiness-oriented forecast evaluation metrics for operational decision systems.

Overview

eb-metrics defines asymmetric, readiness-oriented forecast evaluation primitives (CWSL, NSL, UD, HR@τ, FRS, and related measures). It owns metric math only; evaluation orchestration, policies, and adapters live in sibling packages.

Installation

eb-metrics is distributed as a standard Python package.

pip install eb-metrics

The package supports Python 3.11 and later.

Core Concepts

• Asymmetric error — Overforecasting and underforecasting can have different operational consequences, so evaluation should reflect directional cost differences.
• Interval reliability — In readiness-oriented systems, it matters how often forecasts meet demand within each interval, not just average error over time.
• Shortfall behavior — Underbuilding events are operationally distinct; evaluation should capture both their frequency and their severity.
• Tolerance-based adequacy — Many systems can absorb small deviations; reliability can be expressed as the frequency of “accurate enough” intervals.
• Readiness-oriented evaluation — Forecast quality is assessed by execution feasibility and risk, not solely statistical deviation.

Minimal Example

The following example computes Cost-Weighted Service Loss (CWSL) for a single demand series using asymmetric penalties for underbuild and overbuild:

import numpy as np
from eb_metrics import cwsl

# Realized demand and corresponding forecast
y_true = np.array([20, 28, 32, 35, 40, 42])
y_pred = np.array([22, 25, 29, 36, 37, 45])

# Compute cost-weighted service loss
loss = cwsl(
    y_true=y_true,
    y_pred=y_pred,
    cu=2.0,
    co=1.0,
)

print(loss)

License

BSD 3-Clause License. © 2026 Kyle Corrie.