---
license: apache-2.0
library_name: nanoforecast
pipeline_tag: time-series-forecasting
tags:
  - time-series
  - forecasting
  - time-series-forecasting
  - dart-norm
  - high-cardinality
  - ablation
  - pytorch
  - transformer
  - edge-ai
  - lightweight
  - experimental
metrics:
  - mase
  - smape
  - mae
  - crps
datasets:
  - ETTh1
  - ETTh2
  - ETTm1
  - exchange_rate
  - electricity
  - traffic
---

# NanoForecast v0.6-HC — DART-Norm + High-Cardinality Ablation

**Experimental checkpoint** — DART-Norm (causal mean/std normalization) + HC channel expansion (8 channels for electricity/traffic) + 2x upweighting. Single-session training (100 epochs, ~10h on Colab T4).

## Ablation Results

| Dataset | v0.5 MASE | v0.6-HC MASE | Change |
|---:|---:|---:|---:|
| ETTh1 | 0.681 | 0.834 | +23% worse |
| ETTh2 | 1.110 | 0.760 | **-31% better** |
| ETTm1 | 0.287 | 1.192 | +315% worse |
| exchange_rate | 4.317 | 14.058 | +226% worse |
| electricity | 2.029 | **0.421** | **-79% better** |
| traffic | 1.805 | 2.779 | +54% worse |
| **Overall** | **1.704** | 3.341 | +96% worse |


![Ablation comparison](assets/card_ablation_hc.png)

## Key Findings

- **Electricity win is real**: 0.421 MASE (79% improvement) — beats TimesFM's 0.923
- **ETTh2 improved**: 0.760 MASE (31% improvement)
- **But exchange_rate/ETTm1 collapsed**: DART-Norm too aggressive for some datasets
- **Released v0.5 remains official** — adoption gate failed (traffic worsened)

## Training Details

| Parameter | Value |
|:---|:---|
| USE_DART_NORM | True |
| HC_MAX_CHANNELS | 8 |
| HC_UPWEIGHT | 2x |
| Epochs | 100 |
| Best epoch | 6 |
| Wall time | ~10h (Colab T4) |
| Loss | MultiTaskLoss |

## Next Steps

Ablation notebooks available:
- `colab_training_v06_dart_only.ipynb` — DART-Norm only (no HC)
- `colab_training_v06_hc_only.ipynb` — HC upweighting only (no DART)

---

*Built by [Eulogik](https://eulogik.com) — deployable AI for the real world*