Electric Barometer · Features (eb-features)

Feature engineering primitives for panel-based forecasting systems, designed to integrate seamlessly with the Electric Barometer ecosystem.

Overview

eb-features builds deterministic, panel-aware forecasting features (lags, rolling aggregates, calendar encodings) with explicit entity and time boundaries. It does not train models or evaluate forecasts.

Installation

eb-features is distributed as a standard Python package.

pip install eb-features

The package supports Python 3.11 and later.

Core Concepts

• Panel-aware feature construction — Features are constructed with explicit awareness of entity boundaries and temporal ordering, ensuring correctness in multi-entity forecasting settings.
• Deterministic transformations — Feature generation is designed to be reproducible and free of stochastic behavior, supporting auditability and consistent downstream evaluation.
• Temporal causality — All features respect time directionality, preventing information leakage from future observations into historical feature sets.
• Rolling and lag semantics — Common forecasting features such as lags and rolling aggregates are treated as first-class primitives with clear, well-defined behavior.
• Validation by construction — Feature pipelines include explicit checks and constraints to ensure structural validity before model training or evaluation.

Minimal Example

The example below shows how to construct lagged and rolling features for panel data while preserving entity boundaries and temporal ordering.

import pandas as pd
from eb_features import add_lag_features, add_rolling_features

df = pd.DataFrame({
    "entity_id": ["A", "A", "A", "A", "B", "B", "B", "B"],
    "date": pd.to_datetime(
        ["2024-01-01", "2024-01-02", "2024-01-03", "2024-01-04"] * 2
    ),
    "y": [10, 12, 11, 13, 7, 9, 8, 10],
})
df = df.sort_values(["entity_id", "date"], kind="mergesort")

df, lag_cols = add_lag_features(
    df,
    entity_col="entity_id",
    target_col="y",
    lag_steps=[1, 2],
)

df, roll_cols = add_rolling_features(
    df,
    entity_col="entity_id",
    target_col="y",
    rolling_windows=[3],
    rolling_stats=["mean"],
)

print(lag_cols, roll_cols)
print(df)

License

BSD 3-Clause License. © 2026 Kyle Corrie.