Improve dataset card: add paper link, project page, and task category

README.md

@@ -1,6 +1,13 @@
-# TFRBench Submission Guidelines
+---
+task_categories:
+- time-series-forecasting
+---
 
-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.
+# 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
 
@@ -12,6 +19,10 @@ from huggingface_hub import snapshot_download
 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.
@@ -93,6 +104,6 @@ Each JSON file must be a list of objects. Each object represents a prediction fo
 
 ### 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.
+- `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.