Metadata-Version: 2.1
Name: lazyforecast
Version: 0.0.0
Summary: LazyForecast is a Python library for performing univariate time series analysis using a lazy forecasting approach. This approach is designed to provide quick and simple forecasting models without requiring extensive configuration or parameter tuning.
Home-page: https://github.com/piyushsinghoffice/LazyForecast.git
Author: Piyush Singh
Author-email: piyush.singh.office@gmail.com
License: MIT
Keywords: arima,timeseries,forecasting,pyramid,pmdarima,pyramid-arima,scikit-learn,statsmodels,tensorflow,tensor,machine,learning
Classifier: Development Status :: 1 - Planning
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.9
Classifier: Operating System :: OS Independent
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: ipython
Requires-Dist: matplotlib
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: pmdarima
Requires-Dist: scikit-learn
Requires-Dist: tensorflow
Requires-Dist: tqdm
Provides-Extra: dev
Requires-Dist: twine (>=4.0.2) ; extra == 'dev'


# LazyForecast

LazyForecast is a Python library for performing univariate time series analysis using a lazy forecasting approach. This approach is designed to provide quick and simple forecasting models without requiring extensive configuration or parameter tuning.

# Features

- LazyForecasting automatically selects the best model based on the characteristics of the input time series.
- It supports univariate time series analysis.
- LazyForecasting provides functions for data preprocessing, model training, forecasting, and evaluation.
- It includes various popular forecasting models such as Auto ARIMA, Vanilla LSTM, and RNN.
