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FPA-FOD Weekly
==============

Weekly FPA-FOD aggregates packaged for next-week wildfire count forecasting by size group.

Overview
--------

FPA-FOD Weekly builds rolling lookback windows from weekly wildfire incident counts and predicts next-week counts for grouped size classes.

PyHazards exposes it as a loadable forecasting dataset with a micro mode so sequence models can be validated without the full source archive.

At a Glance
-----------

.. list-table::
   :widths: 28 72
   :stub-columns: 1

   * - Provider
     - Fire Program Analysis Fire-Occurrence Database (FPA-FOD) adaptation in PyHazards
   * - Hazard Family
     - Wildfire
   * - Source Role
     - Weekly Forecasting
   * - Coverage
     - User-provided FPA-FOD coverage
   * - Geometry
     - Temporal tabular sequences
   * - Spatial Resolution
     - Weekly aggregate windows
   * - Temporal Resolution
     - Weekly
   * - Update Cadence
     - User-managed local inputs or deterministic micro mode
   * - Period of Record
     - Depends on the supplied FPA-FOD source files
   * - Formats
     - SQLite, DB, CSV, and Parquet inputs
   * - Inspection CLI
     - ``python -m pyhazards.datasets.fpa_fod_weekly.inspection --micro --lookback-weeks 12``
   * - Registry Entry
     - ``fpa_fod_weekly``

Data Characteristics
--------------------

- Predicts next-week counts for grouped size classes A/B/C/D/EFG.
- Supports feature modes with counts only or counts plus seasonal time features.
- Uses chronological splits to preserve the forecasting setting.
- Returned splits follow the DataBundle contract with sequence inputs and floating-point targets.

Typical Use Cases
~~~~~~~~~~~~~~~~~

- Weekly wildfire forecasting experiments.
- Sequence-model smoke tests for wildfire activity prediction.
- Lightweight benchmarking of tabular temporal wildfire baselines.

Access
------

Use the links below to access the upstream source or its public documentation.

- `PyHazards public dataset catalog </pyhazards_datasets.html>`_

PyHazards Usage
---------------

Use this dataset through the public inspection or registry surface documented below.

Registry Workflow
~~~~~~~~~~~~~~~~~

Primary dataset name: ``fpa_fod_weekly``

.. code-block:: python

   from pyhazards.datasets import load_dataset

   data = load_dataset(
       "fpa_fod_weekly",
       micro=True,
       features="counts+time",
       lookback_weeks=12,
   ).load()

   train = data.get_split("train")
   print(train.inputs.shape, train.targets.shape)

- features='counts' uses only the five weekly count channels.
- features='counts+time' adds sinusoidal week-of-year features for seasonality.

Related Coverage
~~~~~~~~~~~~~~~~

**Benchmarks:** :doc:`Wildfire Benchmark </benchmarks/wildfire_benchmark>`

**Representative Models:** :doc:`DNN-LSTM-AutoEncoder </modules/models_wildfire_fpa>`, :doc:`Wildfire Forecasting </modules/models_wildfire_forecasting>`, :doc:`ASUFM </modules/models_asufm>`

Inspection Workflow
-------------------

Use the documented inspection path below to validate local files before training or analysis.

.. code-block:: bash

   python -m pyhazards.datasets.fpa_fod_weekly.inspection --micro --lookback-weeks 12

Reference
---------

- `PyHazards FPA-FOD weekly adaptation for the wildfire forecasting path. <https://github.com/LabRAI/PyHazards>`_.