Read results

mf.forecast(...) and Experiment.run(...) return ForecastResult.

import macroforecast as mf

result = mf.forecast(
    "fred_md",
    target="INDPRO",
    start="1980-01",
    end="2019-12",
    horizons=[1, 3],
    output_directory="outputs/indpro_default",
)

Date formats: start / end accept ISO date strings: full YYYY-MM-DD, or partial YYYY-MM (normalized to first/last of month), or YYYY (normalized to year-start/year-end).

Use the table accessors for normal analysis:

forecasts = result.forecasts
metrics = result.metrics
ranking = result.ranking
summary = result.mean(metric="mse")

Use cell accessors when you need runtime-level detail:

cells = result.cells
succeeded = result.succeeded

first_cell = result.get(cells[0].cell_id)

Use artifact helpers when the run was executed with output_directory:

manifest_path = result.manifest_path
manifest = result.read_json("manifest.json")
predictions_path = result.file_path("predictions.csv")

result.manifest is the underlying execution result object. result.manifest_path is only available when artifacts were written to disk.