Upload README.md

README.md

@@ -21,19 +21,22 @@ This dataset contains traffic flow data for time series forecasting tasks, commo
 ## Dataset Structure
 
 ### Data Format
+
 - **Format**: Parquet files for efficient loading and analysis
 - **Splits**: train (70%), validation (10%), test (20%) - **temporal splits** preserving chronological order
 - **Features**: Time series traffic flow data with temporal and spatial dimensions
 
 ### Split Strategy
+
 - **Temporal splitting**: Data is split chronologically to prevent data leakage
 - **All sensors included**: Each split contains data for all sensors at each time step
 - **Training period**: Earliest 70% of time samples across all sensors
-- **Validation period**: Next 10% of time samples across all sensors  
+- **Validation period**: Next 10% of time samples across all sensors
 - **Test period**: Latest 20% of time samples across all sensors
 - **Graph structure preserved**: Spatial relationships maintained in all splits
 
 ### Data Schema
+
 - `node_id`: Sensor/node identifier (0-206 for METR-LA, 0-324 for PEMS-BAY)
 - `x_t*_d*`: Input features at different time offsets and dimensions
   - `x_t-11_d0` to `x_t+0_d0`: Traffic flow values at 12 historical time steps
@@ -43,8 +46,9 @@ This dataset contains traffic flow data for time series forecasting tasks, commo
   - `y_t+1_d1` to `y_t+12_d1`: Time-of-day features for prediction horizon
 
 ### Dataset Statistics
+
 - **Total time series samples**: ~34K (METR-LA) / ~52K (PEMS-BAY)
-- **Total records**: ~7M (METR-LA) / ~17M (PEMS-BAY) 
+- **Total records**: ~7M (METR-LA) / ~17M (PEMS-BAY)
 - **Records per sample**: 207 (METR-LA) / 325 (PEMS-BAY) sensors
 - **Temporal resolution**: 5-minute intervals
 - **Prediction horizon**: 1 hour (12 time steps)
@@ -52,24 +56,23 @@ This dataset contains traffic flow data for time series forecasting tasks, commo
 ## Usage
 
 ```python
-from datasets import load_dataset
+from datasets import Dataset, DatasetDict
 import pandas as pd
 
-# Load from Hugging Face Hub
-dataset = load_dataset("witgaw/METR-LA")
-
-# Or load locally
-train_df = pd.read_parquet("train.parquet")
-val_df = pd.read_parquet("val.parquet") 
-test_df = pd.read_parquet("test.parquet")
+# Load from local parquet files
+train_df = pd.read_parquet("METR-LA/train.parquet")
+val_df = pd.read_parquet("METR-LA/val.parquet")
+test_df = pd.read_parquet("METR-LA/test.parquet")
 
-print(f"Train samples: {len(train_df) // 207:,}")  # Divide by number of sensors
-print(f"Total records: {len(train_df):,}")
-print(f"Features per record: {len(train_df.columns)}")
+ds = DatasetDict({
+    "train": Dataset.from_pandas(train_df, preserve_index=False),
+    "val": Dataset.from_pandas(val_df, preserve_index=False),
+    "test": Dataset.from_pandas(test_df, preserve_index=False)
+})
 
-# Example: Get data for first time sample
-first_sample = train_df[train_df.index < 207]  # First 207 records (all sensors)
-print(f"Shape for one time sample: {first_sample.shape}")
+print(f"Train records: {len(ds['train']):,}")
+print(f"Val records: {len(ds['val']):,}")
+print(f"Test records: {len(ds['test']):,}")
 ```
 
 ## Citation
@@ -77,11 +80,11 @@ print(f"Shape for one time sample: {first_sample.shape}")
 If you use this dataset, please cite the original DCRNN paper:
 
 ```bibtex
-@inproceedings{li2018dcrnn,
-  title={Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting},
-  author={Li, Yaguang and Yu, Rose and Shahabi, Cyrus and Liu, Yan},
-  booktitle={International Conference on Learning Representations},
-  year={2018}
+@inproceedings{li2018dcrnn_traffic,
+  title={{Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting}},
+  author={{Li, Yaguang and Yu, Rose and Shahabi, Cyrus and Liu, Yan}},
+  booktitle={{International Conference on Learning Representations}},
+  year={{2018}}
 }
 ```
 
@@ -95,4 +98,4 @@ This dataset is derived from the original METR-LA dataset used in the DCRNN pape
 
 ## License
 
-MIT License - See LICENSE file for details.
+MIT License - See the [original repository LICENSE](https://github.com/liyaguang/DCRNN/blob/master/LICENSE) for details.