---
task_categories:
- time-series-forecasting
---

# TFRBench: A Reasoning Benchmark for Evaluating Forecasting Systems

[Paper](https://huggingface.co/papers/2604.05364) | [Project Page](https://tfrbench.github.io/)

TFRBench is the first benchmark designed to evaluate the reasoning capabilities of forecasting systems. While traditional time-series forecasting evaluations focus solely on numerical accuracy, TFRBench provides a protocol for evaluating the reasoning generated by models—specifically their analysis of cross-channel dependencies, trends, and external events. The benchmark spans ten datasets across five diverse domains.

## How to Download the Data

You can download the dataset using the `huggingface_hub` library:

```python
from huggingface_hub import snapshot_download
# Download the entire repository
snapshot_download(repo_id="AtikAhamed/TFRBench", repo_type="dataset", local_dir="./my_local_data")
```

# TFRBench Submission Guidelines

Thank you for your interest in TFRBench! To participate in the leaderboard, please follow the directory structure and schema below to format your model predictions.

## Public Inputs (What you receive)

You will be provided with public input JSON files. Each file is a list of objects containing historical data and the timestamps for which you need to predict.

### Public Input Schema example:

```json
[
  {
    "id": "NYC_Taxi_0",
    "dataset": "NYC_Taxi",
    "historical_window": {
      "index": ["2009-01-09 00:00:00", ...],
      "columns": ["Trip_Count"],
      "data": [[19000], ...]
    },
    "future_window_timestamps": ["2009-01-13 00:00:00", ...]
  }
]
```

## Submission Directory Structure (What you submit)

Your submission should be a directory containing JSON files for each dataset. It is required to include all datasets.

```text
my_submission/
├── metadata.json          
├── NYC_Taxi.json
├── amazon.json
└── ...
```

## How to Submit

Please use this form to submit your predictions: https://forms.gle/gNqKrmw7hawY5VK99

## Metadata Schema

To display your model name and provide a link to your paper or project on the leaderboard, include a `metadata.json` file at the root of your submission directory.

```json
{
  "model_name": "My Awesome Model",
  "link": "https://github.com/myuser/myproject",
  "description": "Optional description"
}
```


## File Schema

Each JSON file must be a list of objects. Each object represents a prediction for a single sample.

```json
[
  {
    "id": "solar_daily_0",
    "Reasoning": "The trend will continue upwards due to clear summer skies. Weekend dips are expected.",
    "Prediction": [
      [2.5],
      [2.6],
      [2.4],
      ...
    ]
  },
  {
    "id": "solar_daily_1",
    "Reasoning": "Consistent stable pattern...",
    "Prediction": [
      [1.1],
      [1.1],
      [1.1],
      ...
    ]
  }
]
```

### Required Fields:

- `id` (String): The unique identifier for the sample (must match the ID provided in public inputs).
- `Reasoning` (String): The text explanation generated by your model.
- `Prediction` (List of Lists): A 2D numerical array representing the forecast window. For single-channel datasets, use `[[value]]` per time step.