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README.md

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+# Dataset Repository
+
+This repository includes several datasets: **Houston Crime Dataset**, **Tourism in Australia**, **Prison in Australia**, and **M5**. These datasets consist of time series data representing various metrics across different categories and groups.
+
+## Dataset Structure
+
+Each dataset is divided into training and prediction sets, with features such as groups, indices, and time series data. Below is a general overview of the dataset structure:
+
+### Training Data
+The training data contains time series data with the following structure:
+- **x_values**: List of time steps.
+- **groups_idx**: Indices representing different group categories (e.g., Crime, Beat, Street, ZIP for Houston Crime).
+- **groups_n**: Number of unique values in each group category.
+- **groups_names**: Names corresponding to group indices.
+- **n**: Number of time series.
+- **s**: Length of each time series.
+- **n_series_idx**: Indices of the time series.
+- **n_series**: Indices for each series.
+- **g_number**: Number of group categories.
+- **data**: Matrix of time series data.
+
+### Prediction Data
+The prediction data has a similar structure to the training data and is used for forecasting purposes. 
+
+**Note:** It contains the complete data, including training and predict.
+
+### Additional Metadata
+- **seasonality**: Seasonality of the data.
+- **h**: Forecast horizon.
+- **dates**: Timestamps corresponding to the time steps.
+
+## Example Usage
+
+Below is an example of how to load and use the datasets using the `datasets` library:
+
+```python
+import pickle
+
+def load_pickle(file_path):
+    with open(file_path, 'rb') as file:
+        data = pickle.load(file)
+    return data
+
+# Paths to your datasets
+m5_path = 'path/to/m5.pkl'
+police_path = 'path/to/police.pkl'
+prison_path = 'path/to/prison.pkl'
+tourism_path = 'path/to/tourism.pkl'
+
+m5_data = load_pickle(m5_path)
+police_data = load_pickle(police_path)
+prison_data = load_pickle(prison_path)
+tourism_data = load_pickle(tourism_path)
+
+# Example: Accessing specific data from the datasets
+print("M5 Data:", m5_data)
+print("Police Data:", police_data)
+print("Prison Data:", prison_data)
+print("Tourism Data:", tourism_data)
+
+# Access the training data
+train_data = prison_data["train"]
+
+# Access the prediction data
+predict_data = prison_data["predict"]
+
+# Example: Extracting x_values and data
+x_values = train_data["x_values"]
+data = train_data["data"]
+
+print(f"x_values: {x_values}")
+print(f"data shape: {data.shape}")
+```
+
+### Steps to Follow:
+
+1. **Clone the Repository:**
+   ```sh
+   git clone https://huggingface.co/datasets/zaai-ai/hierarchical_datasets.git
+   cd hierarchical_datasets
+   ```
+2. **Update the File Paths:**
+   - Ensure the paths to the .pkl files are correct in your Python script.
+
+3. **Load the Datasets:**
+   - Use the `pickle` library in Python to load the `.pkl` files.
+