semvec/pypi-packages huggingface dataset:
forecastify 0.1
A package for time series forecasting using ARIMA, SARIMA, and Exponential Smoothing
Requires Python: >=3.7

# Forecasting Package

The Forecasting Package provides robust time series forecasting capabilities using ARIMA, SARIMA, and Exponential Smoothing models. It includes functionalities for model optimization using Optuna and visualizing forecasts with Matplotlib.

## Features

- **ARIMA**: Autoregressive Integrated Moving Average model for forecasting.
- **SARIMA**: Seasonal ARIMA model for handling seasonal patterns in time series data.
- **Exponential Smoothing**: Includes options for additive and multiplicative seasonal effects.
- **Model Optimization**: Use Optuna to find the best hyperparameters for your models.
- **Visualization**: Generate and save plots of the forecasted data.

## Installation

To install the package, use pip: 

```bash
pip install git+https://github.com/Wasiue03/Forecasting-Python-Package.git

"""
Usage
from forecasting.forecast import main_forecasting'

forecast_series = main_forecasting(
    file_path='path/to/your/data.csv',
    column_name='your_column',
    model_type='arima',
    steps=10,
    optimize=True,
    plot=True,
    plot_path='arima_forecast.png'
)


forecast_series = main_forecasting(
    file_path='data/metrics.csv',
    column_name='value',
    model_type='sarima',
    steps=10,
    optimize=True,
    plot=True,
    plot_path='sarima_forecast.png'
)


forecast_series = main_forecasting(
    file_path='data/metrics.csv',
    column_name='value',
    model_type='exponential_smoothing',
    steps=10,
    optimize=True,
    plot=True,
    plot_path='exponential_smoothing_forecast.png'
)

"""
"""
Contributing:

If you would like to contribute to the development of this package, please fork the repository and submit a pull request. For detailed instructions on contributing, please refer to the CONTRIBUTING.md file.

"""
forecaste 0.1
A package for time series forecasting using ARIMA, SARIMA, and Exponential Smoothing
Requires Python: >=3.7

# Forecasting Package

The Forecasting Package provides robust time series forecasting capabilities using ARIMA, SARIMA, and Exponential Smoothing models. It includes functionalities for model optimization using Optuna and visualizing forecasts with Matplotlib.

## Features

- **ARIMA**: Autoregressive Integrated Moving Average model for forecasting.
- **SARIMA**: Seasonal ARIMA model for handling seasonal patterns in time series data.
- **Exponential Smoothing**: Includes options for additive and multiplicative seasonal effects.
- **Model Optimization**: Use Optuna to find the best hyperparameters for your models.
- **Visualization**: Generate and save plots of the forecasted data.

## Installation

To install the package, use pip: 

```bash
pip install git+https://github.com/Wasiue03/Forecasting-Python-Package.git

"""
Usage
from forecasting.forecast import main_forecasting'

forecast_series = main_forecasting(
    file_path='path/to/your/data.csv',
    column_name='your_column',
    model_type='arima',
    steps=10,
    optimize=True,
    plot=True,
    plot_path='arima_forecast.png'
)


forecast_series = main_forecasting(
    file_path='data/metrics.csv',
    column_name='value',
    model_type='sarima',
    steps=10,
    optimize=True,
    plot=True,
    plot_path='sarima_forecast.png'
)


forecast_series = main_forecasting(
    file_path='data/metrics.csv',
    column_name='value',
    model_type='exponential_smoothing',
    steps=10,
    optimize=True,
    plot=True,
    plot_path='exponential_smoothing_forecast.png'
)

"""
"""
Contributing:

If you would like to contribute to the development of this package, please fork the repository and submit a pull request. For detailed instructions on contributing, please refer to the CONTRIBUTING.md file.

"""