---
license: cc-by-4.0
---

# 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.