Metadata-Version: 2.1
Name: autoforecast
Version: 0.0.9
Summary: AutoML time series forecasting
Home-page: https://github.com/GuillaumeSimo/autoforecast
Author: Guillaume Simo
Author-email: guillaume.simo@hotmail.fr
License: UNKNOWN
Description: # Auto Forecast
        
        AutoML library for time series forecasting
        
        ## Quick start
        
        Upgrade pip
        ```bash
        $ pip install pip --upgrade
        ```
        
        Install autoforecast
        ```bash
        $ pip install autoforecast
        ```
        
        
        ### Try it out with your own dataset
        
        1. Preprocessing
        
        ```python
        from Autoforecast.preprocessing import preprocessing
        
        X_train, y_train, X_test, y_test = preprocessing(
            df=df,
            target_name=['sales'],
            categoricals=['store_id', 'dpt_id', 'holiday', 'zipcode', ...],
            numericals=['employee_num', 'store_surface', ...],
            date_col=['date'],
            train_size=0.8,
            engineering=True,
            selection=True
        )
        ```
        
        2. Fitting and predicting
        
        ```python
        from autoforecast.automl import AutoForecast
        
        
        model = AutoForecast()
        
        print('Autoforecast() model fitting...')
        model.fit(X_train=X_train, y_train=y_train)
        
        print('Autoforecast() model predicting...')
        
        y_pred = model.predict(X_test=X_test)
        print(f'y_pred={y_pred})
        ```
        
        
        ### Run the example function
        
        ```python
        from autoforecast.examples import autoforecast_bitcoin
        
        
        autoforecast_bitcoin.run()
        ```
        
        
        ### Fetch historical cryptocurrency data
        
        This function is a wrapper of https://developers.coinbase.com/api/v2#prices
        * ***n***: integer, number of days we want since today
        * **type**: str, ['buy', 'sell', 'spot']
        * **currency_pair**: str, crypto & currency
        
        ```python
        from autoforecast.datasets.import_bitcoin_price import get_price_for_last_n_days
        
        
        crypto_df = get_price_for_last_n_days(
            n=1, type='spot', currency_pair='BTC-USD')
        )
        ```
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
