forecastops 0.2.1

Local-first observability, evaluation, and UI for production forecasts.

ForecastOps

ForecastOps is a local-first observability and evaluation layer for production forecasts. It works with the forecasting code you already have.

Add one line after .predict(), then run fops ui.

import forecastops as fops

forecast = model.predict(future)

run = fops.capture(
    forecast,
    project="site-traffic",
    series_id="homepage",
    cutoff=train_df["ds"].max(),
    actuals=actuals_df,
)

fops ui

ForecastOps stores forecast artifacts locally as Parquet, writes run metadata to DuckDB, computes horizon-aware metrics, generates static HTML reports, and serves a read-only local UI. It does not train forecasting models, require a cloud account, or upload raw forecast data.

Install

From PyPI:

pip install forecastops

From source:

git clone https://github.com/Parisi-Labs/forecastops.git
cd forecastops
pip install -e .

Quickstart

python -m venv .venv
source .venv/bin/activate
pip install -e .
python examples/generic_dataframe.py
fops report --latest
fops ui

Open http://127.0.0.1:4784 after starting the UI.

Local UI

fops ui serves a read-only explorer for the local store:

• Runs — every captured run with horizon, points, MAE, WAPE, bias, coverage, skill, and validation status; filterable and sortable.
• Run detail — headline metrics, a forecast inspector with one chart per series, and a diagnostics cockpit: residual distribution, error by horizon, per-series worst offenders, and per-regime breakdowns — plus metrics, validation, residuals, artifacts, and the capture trace timeline.
• Projects — runs grouped by project with error trends across captures.
• Groups — experiment and backtest groups with run counts and mean error; open a group to see per-metric mean ± std and stability across its runs.
• Compare — metric deltas and regressions between any two runs, backed by fops diff.

Core Concepts

• capture: normalize forecasts from existing workflows.
• ForecastSchema: map arbitrary dataframe columns to canonical semantics.
• validate: catch schema, timestamp, duplicate, interval, and leakage issues.
• evaluate: compute MAE, RMSE, WAPE, sMAPE, bias, coverage, interval width, pinball loss (for quantile forecasts), and count — sliced by horizon and by any categorical columns you keep (e.g. region, holiday_flag, event_type).
• compare: calculate benchmark metrics and skill.
• backtest: evaluate a rolling-origin forecast panel as one grouped run set, with per-cutoff and aggregate (mean/std) metrics.
• diff: compare two captured runs.
• diagnose: a machine-readable diagnosis of a run — overall metrics, skill, worst horizons/series/regimes, validation, and artifact URIs — for agents and scripts (fops diagnose <run_id>).
• groups: tag related runs with capture(group=...) (or a backtest) and browse them together in the UI.
• local store: .forecastops/forecastops.duckdb plus Parquet artifacts.
• UI: local read-only browser explorer for runs, metrics, residuals, validation, artifacts, and run differences.

Privacy Defaults

ForecastOps is local-first by default:

• binds the UI to 127.0.0.1 and refuses other hosts unless you pass --allow-remote
• makes no outbound network calls
• stores raw forecast points in the configured local store
• emits OpenTelemetry only when explicitly enabled
• avoids raw forecast points in telemetry

Development

pip install -e ".[dev]"
pytest
ruff check .
mypy forecastops

License

Apache-2.0. See LICENSE.