Update README

README.md

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 ---
-<<<<<<< HEAD
 license: apache-2.0        # 或 mit, cc-by-4.0 等
 task_categories:
 - time-series-forecasting  # 这是让系统识别为时间序列任务的关键
 tags:                      # 自定义标签
-=======
-license: apache-2.0
-task_categories:
-- time-series-forecasting
-tags:
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 - time-series
 - forecasting
 - benchmark
@@ -18,77 +11,12 @@ language:
 - en
 ---
 
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-
-# **TIME**
-
-## 📂Files
-**Data**
-=======
->>>>>>> 652ddfc (Refactor: Move data)
-
 # **TIME**
 
 We present TIME, a task-centric benchmark comprising 50 fresh datasets and 98 forecasting tasks, where configurations are aligned with real-world operational requirements. By archiving window-level prediction results, our benchmark enables pattern-level evaluation and visualization. This repository contains the ready-to-use Hugging Face dataset for running experiments.
 
 [🏆Leaderboard](https://huggingface.co/spaces/Real-TSF/TIME-leaderboard)
 
-<<<<<<< HEAD
-**Outputs and Results**
-
-The `output/` directory contains the extracted time series features (tsfeatures) for each variate and the detailed forecasting results for every experiment.
-
-**Note**: These files are for building leaderboard and visualization; users do not need to download this directory.
-
-* `features/`: **Statistical Features (tsfeatures)**
-
-    Each dataset's features are saved to: `output/features/{dataset}/{freq}/`.
-    This directory stores the computed tsfeatures for the variates in the dataset. The folder contains a CSV file named either:
-    * `test.csv`: Features calculated on the **test split**.
-    * `full.csv`: Features calculated on the **full dataset**.
-
-* `results/`: **Experimental Results**
-
-    Each experiment is saved to: `output/results/{model}/{dataset}/{freq}/{horizon}/`.
-    Each directory represents a specific model-dataset-frequency-horizon combination and contains the following files:
-    * `config.json`: The experiment settings and hyperparameters.
-    * `metrics.npz`: Window-level forecasting metrics (e.g., MASE, CRPS).
-    * `predictions.npz`: Quantile prediction results for each sliding window.
-
-## ⬇️Download
-
-### Download Only the Dataset
-Since the repository contains extensive experimental results in the `output` folder, we recommend downloading **only** the dataset folder to save bandwidth and storage.
-
-You can use the `huggingface_hub` library to download specifically the `data/hf_dataset` directory:
-
-```python
-from huggingface_hub import snapshot_download
-from datasets import load_from_disk
-
-# Download only the 'data/hf_dataset' folder
-# This skips the heavy 'output' folder
-folder_path = snapshot_download(
-    repo_id="TIME-benchmark/TIME-1.0",
-    repo_type="dataset",
-    allow_patterns="data/hf_dataset/*",
-    local_dir="./"  # Downloads to your current directory
-)
-```
-
-If you prefer using the command line, we strongly recommend using Git Sparse Checkout. This allows you to clone the repository structure without downloading the heavy output directory, saving significant bandwidth.
-```bash
-# 1. Clone the repository without downloading files initially
-git clone --depth 1 --filter=blob:none --sparse https://huggingface.co/datasets/TIME-benchmark/TIME-1.0
-
-# 2. Navigate to the repository
-cd TIME-1.0
-
-# 3. Configure git to only download the 'data/hf_dataset' folder
-git sparse-checkout set data/hf_dataset
-```
-=======
 [📂Processed csv](https://huggingface.co/datasets/Real-TSF/TIME-ProcessedCSV) (Optional: For reference or custom pipelines only)
->>>>>>> 652ddfc (Refactor: Move data)
 
 [📊Reuslts & tsfeatures](https://huggingface.co/datasets/Real-TSF/TIME-Output) (Optional: Leaderboard data, not required for experiments)