Scaled Forecasting with Python and R – With Forecasting for Several Different Types of Models and Time Series

Scaled Forecasting with Python and R – With Forecasting for Several Different Types of Models and Time Series
English | Size: 1.87 GB
Category: Programming | E-learning | others

In this video, you will explore forecasting techniques in Python, including how to use machine learning models from Scikit Learn, as well as integrating R as a sub process to gain access to the robust forecast library, incorporating the auto.arima and tbats models. We create our own Python class called Forecaster that stores all of the relevant information about the predictions, including error metrics, model forms, hyperparameter selections, and the forecasts themselves. The module is written in such a way that models can easily be added to the framework for increasing accuracy.

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