Forecasting using non-linear models with sktime

In the previous recipes, you had to prepare the time series data to make it suitable for supervised ML. This is because scikit-learn (sklearn) is a general ML library and not specific for time series forecasting. This is where sktime is positioned to fill in the gap as a unified machine learning framework for time series. In this recipe, you will explore how to create a ML pipeline that prepares any time series data and can use algorithms from a standard ML library such as sklearn.

In Chapter 13, Deep Learning for Time Series Forecasting, you will explore other non-linear models, such as Recurrent Neural Networks. In this recipe, you will explore different algorithms that can capture non-linear relationships such as K-Nearest Neighbors Regression.

How to do it...

You will train multiple regressors (linear and non-linear) from sklearn. The recipe will cover data preparation, model training, forecasting, and comparing performance...