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---
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language:
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- en
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license:
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- cc-by-4.0
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size_categories:
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- 10K<n<100k
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task_categories:
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- time-series-forecasting
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task_ids:
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- univariate-time-series-forecasting
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- multivariate-time-series-forecasting
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---
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# Dataset Repository
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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.
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## Dataset Structure
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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:
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### Training Data
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The training data contains time series data with the following structure:
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- **x_values**: List of time steps.
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- **groups_idx**: Indices representing different group categories (e.g., Crime, Beat, Street, ZIP for Houston Crime).
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- **groups_n**: Number of unique values in each group category.
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- **groups_names**: Names corresponding to group indices.
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- **n**: Number of time series.
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- **s**: Length of each time series.
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- **n_series_idx**: Indices of the time series.
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- **n_series**: Indices for each series.
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- **g_number**: Number of group categories.
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- **data**: Matrix of time series data.
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### Prediction Data
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The prediction data has a similar structure to the training data and is used for forecasting purposes.
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**Note:** It contains the complete data, including training and prediction sets.
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### Additional Metadata
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- **seasonality**: Seasonality of the data.
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- **h**: Forecast horizon.
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- **dates**: Timestamps corresponding to the time steps.
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## Example Usage
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Below is an example of how to load and use the datasets using the `datasets` library:
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```python
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import pickle
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def load_pickle(file_path):
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with open(file_path, 'rb') as file:
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data = pickle.load(file)
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return data
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# Paths to your datasets
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m5_path = 'path/to/m5.pkl'
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police_path = 'path/to/police.pkl'
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prison_path = 'path/to/prison.pkl'
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tourism_path = 'path/to/tourism.pkl'
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m5_data = load_pickle(m5_path)
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police_data = load_pickle(police_path)
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prison_data = load_pickle(prison_path)
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tourism_data = load_pickle(tourism_path)
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# Example: Accessing specific data from the datasets
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print("M5 Data:", m5_data)
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print("Police Data:", police_data)
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print("Prison Data:", prison_data)
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print("Tourism Data:", tourism_data)
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# Access the training data
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train_data = prison_data["train"]
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# Access the prediction data
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predict_data = prison_data["predict"]
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# Example: Extracting x_values and data
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x_values = train_data["x_values"]
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data = train_data["data"]
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print(f"x_values: {x_values}")
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print(f"data shape: {data.shape}")
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```
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### Steps to Follow:
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1. **Clone the Repository:**
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```sh
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git clone https://huggingface.co/datasets/zaai-ai/hierarchical_time_series_datasets.git
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cd hierarchical_time_series_datasets
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```
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2. **Update the File Paths:**
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- Ensure the paths to the .pkl files are correct in your Python script.
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3. **Load the Datasets:**
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- Use the `pickle` library in Python to load the `.pkl` files.
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