elpa

name	elpa
description	Orchestrate real ELPA-style ensemble forecasting workflows by triggering external sub-model training jobs (for example PyTorch/Prophet/TiDE/transformers), then computing ELPA online/offline weights from validation errors. Use when you need production-oriented ensemble training instead of lightweight simulation adapters.

ELPA

Overview

This skill does not train toy adapters. It triggers real sub-model training commands from your own training codebases and then builds ELPA routing/weights from real validation errors.

Default model pool is intentionally larger than 4 and can be expanded freely.

Workflow

1. Prepare a training config JSON (see assets/elpa_train_template.json).
2. Dry-run the command plan to verify all sub-model commands.
3. Execute real sub-model training when resources are available.
4. Prepare validation error inputs per model.
5. Build ELPA ensemble policy JSON from those errors.

1) Prepare Config

Create a config based on assets/elpa_train_template.json.

• Put your real training entrypoints in each model train_cmd.
• Keep each model tagged as online or offline.
• Add as many models as needed; ELPA is not limited to 4.

2) Dry-Run Plan (No Training)

python3 scripts/elpa_orchestrator.py \
  --config assets/elpa_train_template.json \
  --run-dir .runtime/elpa_run \
  --manifest-out .runtime/elpa_run/train_manifest.json

This prints and records the commands that would run, without training.

3) Execute Real Training

python3 scripts/elpa_orchestrator.py \
  --config /path/to/your_train_config.json \
  --run-dir .runtime/elpa_run \
  --manifest-out .runtime/elpa_run/train_manifest.json \
  --execute

Use this only in an environment that has the required ML dependencies and hardware.

4) Build ELPA Integration Policy

After each sub-model produces validation errors, run:

python3 scripts/elpa_integrator.py \
  --config /path/to/your_integrate_config.json \
  --output .runtime/elpa_run/elpa_policy.json

The output includes:

• scores for each model from validation errors
• online_weights and offline_weights
• best_online_model and best_offline_model
• ELPA control fields (beta, dirty_interval, amplitude_window, mutant_epsilon)

Model Scaling

To support more models, append model blocks in your config with:

• unique name
• group as online or offline
• real train_cmd

No script changes are needed for adding models.

Files

• scripts/elpa_orchestrator.py: real sub-model training command planner/executor
• scripts/elpa_integrator.py: ELPA score/weight builder from validation errors
• assets/elpa_train_template.json: >4-model real training template
• assets/elpa_integrate_template.json: ELPA integration template
• references/config-schema.md: config field reference and placeholders