config-h/WLCR-SEA-Predictor

WLCR-SEA Predictor

WLCR-SEA (Window-Local Context Representation with Seasonal Expert Attention) forecasts cellular traffic using only the target cell's request history and frozen model assets. Each request contains an ordered 336-hour history and observation mask. The model forecasts the next 24 hours for four indicators:

1. uplink active users;
2. downlink active users;
3. average used downlink PRBs;
4. average used uplink PRBs.

The two PRB fields are counts of average used physical resource blocks, not utilization percentages. WLCR-SEA routes between eight seasonal experts with hard availability masking, reliability-aware Entmax routing, and a bounded residual.

Public five-model predictor

The checkpoints/ directory contains the five members used by the public predictor. Each checkpoint stores schema_version, experiment_version, its seed, selected configuration and epoch, a frozen (24, 4) training prior, and the CPU state_dict. SHA256SUMS records their integrity hashes.

The artifact filenames retain the paper's internal experiment identifier so that the release can be mapped exactly to research manifests:

File	Seed	Selected configuration	Epoch
A6_mixed_aug_seed42.pt	42	d16_h32_lr1e3_delta025	67
A6_mixed_aug_seed43.pt	43	d32_h64_lr5e4_delta050	40
A6_mixed_aug_seed44.pt	44	d16_h32_lr1e3_delta025	65
A6_mixed_aug_seed45.pt	45	d32_h64_lr5e4_delta050	56
A6_mixed_aug_seed46.pt	46	d32_h64_lr5e4_delta050	65

The reader-facing name is WLCR-SEA five-model ensemble. Its output is the arithmetic mean of the five member forecasts after inverse transformation to linear traffic space.

Load the same predictor as the Demo

Clone the implementation repository, install requirements-demo.txt, and use the verified ensemble loader:

from demo.model_loader import load_ensemble
from demo.runtime import run_forecast

ensemble = load_ensemble()
result = run_forecast("request.csv", ensemble=ensemble)
forecast = result.prediction  # shape: (24, 4), linear traffic space

The public runtime pins model revision eb4447f4ebab, verifies every checkpoint SHA-256, and checks model metadata before loading. Request parsing, observation-mask handling, five-member aggregation, and audit export all use the same code path as the public Demo.

Evaluation snapshot

On the project's reported holdout workflow, the five-model predictor reports macro-cell WAPE 0.177612, pooled WAPE 0.184915, and macro-indicator WAPE 0.195511. These are different aggregations of one dataset and protocol, and they are not general deployment guarantees.

Intended use and limitations

This release supports research and reproducibility for request-local cellular traffic forecasting. It is not a general-purpose pretrained time-series model and should be validated on the target network before operational use. Training data is not included, and the source dataset's terms are separate from this model release. PyTorch .pt files use pickle-based serialization; load only trusted files and verify their checksums.

Routing weights describe internal allocation. They are not causal effects, feature attributions, or calibrated uncertainty estimates.

License

Apache License 2.0. See LICENSE.