Card Long Term Forecasting Eval

---
name: card-long-term-forecasting-eval
description: Evaluates multivariate time series forecasting models on capturing temporal and cross-channel dependencies across multiple real-world benchmarks. It probes the model's ability to predict future values over varying horizons using fixed historical lookback windows. Use when the user wants to benchmark on ETTm1, ETTm2, ETTh1, ETTh2, Weather, Electricity, Traffic, or asks about evaluating this task. Reports MSE.
metadata:
  skill_kind: dataset_eval
  source_arxiv: 2305.12095
  bibtex_key: wang2023card
  confidence: high
---

# card-long-term-forecasting-eval

> CARD: Channel Aligned Robust Blend Transformer for Time Series Forecasting — Wang Xue et al. (2023) (arXiv:2305.12095, 2023)

## What this evaluates

Evaluates multivariate time series forecasting models on capturing temporal and cross-channel dependencies across multiple real-world benchmarks. It probes the model's ability to predict future values over varying horizons using fixed historical lookback windows.

## Datasets

- **ETTm1** — total ?; splits: test (-1)
- **ETTm2** — total ?; splits: test (-1)
- **ETTh1** — total ?; splits: test (-1)
- **ETTh2** — total ?; splits: test (-1)
- **Weather** — total ?; splits: test (-1)
- **Electricity** — total ?; splits: test (-1)
- **Traffic** — total ?; splits: test (-1)

## Metrics

- `MSE` **(primary)** — range: other
  - Mean Squared Error: the average of the squared differences between predicted and actual values. Lower is better.
- `MAE` — range: other
  - Mean Absolute Error: the average of the absolute differences between predicted and actual values. Lower is better.

## Input / output format

**Input**: Multivariate time series sequences with a fixed lookback length of 96 time steps.

**Output**: Predicted values for multiple forecasting horizons (96, 192, 336, 720 time steps).

## Scoring recipe

```python
def compute_metrics(y_true, y_pred):
    mse = np.mean((y_true - y_pred) ** 2)
    mae = np.mean(np.abs(y_true - y_pred))
    return mse, mae
# Final reported score = average of compute_metrics() over 10 independent random seeds and 4 prediction horizons (96, 192, 336, 720).
```

## Common pitfalls

- Failing to apply reversible instance normalization (RevIN) to handle data heterogeneity, which breaks fair comparison with baselines.
- Using inconsistent lookback lengths across experiments, as the protocol strictly fixes the lookback at 96 for standard comparison.
- Reporting single-run results instead of averaging over 10 independent repeats as mandated by the evaluation protocol.

## Evidence (verbatim from paper)

> Each setting is repeated 10 times and average MSE/MAE results are reported. The results are summarized in Table 1. Regarding the average performance across four different output horizons, CARD gains the best performance in 6 out of 7 and 7 out of 7 in MSE and MAE, respectively.

## Citation

```bibtex
@misc{wang2023card,
  title={CARD: Channel Aligned Robust Blend Transformer for Time Series Forecasting},
  author={Wang Xue et al. (2023)},
  year={2023},
  note={arXiv:2305.12095}
}
```

- arXiv: 2305.12095