README.md

---
license: mit
task_categories:
  - time-series-forecasting
language:
  - en
tags:
  - time-series
  - forecasting
  - benchmark
  - synthetic
  - diagnostics
size_categories:
  - 100K<n<1M
configs:
  - config_name: F1_AR1
    data_files:
      - split: train
        path: F1_AR1/train.parquet
      - split: test
        path: F1_AR1/test.parquet
  - config_name: F1_ARFIMA
    data_files:
      - split: train
        path: F1_ARFIMA/train.parquet
      - split: test
        path: F1_ARFIMA/test.parquet
  - config_name: F2_OU
    data_files:
      - split: train
        path: F2_OU/train.parquet
      - split: test
        path: F2_OU/test.parquet
  - config_name: F2_GBM
    data_files:
      - split: train
        path: F2_GBM/train.parquet
      - split: test
        path: F2_GBM/test.parquet
  - config_name: F3_fBm
    data_files:
      - split: train
        path: F3_fBm/train.parquet
      - split: test
        path: F3_fBm/test.parquet
  - config_name: F4_LogisticMap
    data_files:
      - split: train
        path: F4_LogisticMap/train.parquet
      - split: test
        path: F4_LogisticMap/test.parquet
  - config_name: F4_MackeyGlass
    data_files:
      - split: train
        path: F4_MackeyGlass/train.parquet
      - split: test
        path: F4_MackeyGlass/test.parquet
  - config_name: F5_GP_RBF
    data_files:
      - split: train
        path: F5_GP_RBF/train.parquet
      - split: test
        path: F5_GP_RBF/test.parquet
  - config_name: F5_GP_Matern
    data_files:
      - split: train
        path: F5_GP_Matern/train.parquet
      - split: test
        path: F5_GP_Matern/test.parquet
  - config_name: F5_GP_Periodic
    data_files:
      - split: train
        path: F5_GP_Periodic/train.parquet
      - split: test
        path: F5_GP_Periodic/test.parquet
  - config_name: F6_ChangePoint
    data_files:
      - split: train
        path: F6_ChangePoint/train.parquet
      - split: test
        path: F6_ChangePoint/test.parquet
  - config_name: F6_RegimeSwitching
    data_files:
      - split: train
        path: F6_RegimeSwitching/train.parquet
      - split: test
        path: F6_RegimeSwitching/test.parquet
  - config_name: F7_Fourier
    data_files:
      - split: train
        path: F7_Fourier/train.parquet
      - split: test
        path: F7_Fourier/test.parquet
  - config_name: F7_Polynomial
    data_files:
      - split: train
        path: F7_Polynomial/train.parquet
      - split: test
        path: F7_Polynomial/test.parquet
  - config_name: F8_VAR
    data_files:
      - split: train
        path: F8_VAR/train.parquet
      - split: test
        path: F8_VAR/test.parquet
---

# TSDiag: A Decomposable Synthetic Benchmark for Diagnosing Time-Series Forecasting Models

## Dataset Description

TSDiag is a synthetic time-series benchmark designed for **diagnosing** forecasting models beyond simple ranking. Each generator has a known Bayes-optimal predictor, enabling decomposition of forecasting error into four identifiable components: irreducible noise, architecture bottleneck, data insufficiency, and optimization variance.

## Dataset Structure

The dataset contains **15 generators** across **8 process families**:

| Family | Generators | Key Property |
|--------|-----------|--------------|
| F1: Linear Autoregressive | AR1, ARFIMA | Stationary linear dependence |
| F2: Stochastic Differential Equation | OU, GBM | Continuous-time dynamics |
| F3: Long Memory | fBm | Self-similar, long-range dependence |
| F4: Deterministic Chaos | LogisticMap, MackeyGlass | Sensitivity to initial conditions |
| F5: Gaussian Process | GP_RBF, GP_Matern, GP_Periodic | Kernel-defined covariance |
| F6: Non-stationary | ChangePoint, RegimeSwitching | Regime changes |
| F7: Trend + Seasonality | Fourier, Polynomial | Deterministic patterns |
| F8: Multivariate Causal | VAR | Cross-variable dependence |

## Data Format

Each configuration (generator) contains:
- **train.parquet**: 10,000 samples in wide format
- **test.parquet**: 500 samples in wide format

### Column schema (univariate):
`sample_id, t_0, t_1, ..., t_287`

### Column schema (multivariate, e.g., F8_VAR with 3 channels):
`sample_id, t_0_c0, t_0_c1, t_0_c2, t_1_c0, ..., t_287_c2`

### Temporal structure:
- **Context** (input): first 192 time steps (`t_0` to `t_191`)
- **Horizon** (target): last 96 time steps (`t_192` to `t_287`)

## Configuration

| Parameter | Value |
|-----------|-------|
| Context length | 192 |
| Forecast horizon | 96 |
| Total length | 288 |
| Train samples per generator | 10,000 |
| Test samples per generator | 500 |
| Train seed | 42 |
| Test seed | 9999 |

## Usage

```python
from datasets import load_dataset

# Load a specific generator
ds = load_dataset("YOUR_USERNAME/tsdiag", "F1_AR1")

# Access train/test splits
train_df = ds["train"].to_pandas()
test_df = ds["test"].to_pandas()

# Extract context and horizon
context_cols = [f"t_{i}" for i in range(192)]
horizon_cols = [f"t_{i}" for i in range(192, 288)]

X_context = train_df[context_cols].values  # (10000, 192)
Y_horizon = train_df[horizon_cols].values  # (10000, 96)
```

## Citation

```bibtex
@inproceedings{tsdiag2026,
  title={TSDiag: A Decomposable Synthetic Benchmark for Diagnosing Time-Series Forecasting Models},
  author={Anonymous},
  booktitle={NeurIPS 2026 Datasets and Benchmarks Track},
  year={2026}
}
```

## License

MIT