Forecasting Using Supervised Machine Learning
In this chapter, you will explore how to apply different Machine Learning (ML) algorithms for time-series forecasting. ML algorithms can be categorized into supervised, unsupervised, and reinforcement learning. Here, we will focus on supervised ML, where the goal is to learn from past observations to predict future values.
The first recipe will guide you through the critical process of preparing a time series for supervised ML. Since time-series data is sequential, we need to transform it to make it suitable for ML models, which typically expect a tabular format.
Furthermore, you will work with two powerful ML libraries: scikit-learn and sktime. Scikit-learn (often called sklearn) is a popular ML library in Python that offers a wide range of algorithms for supervised and unsupervised learning and a plethora of tools for data preprocessing, as well as model evaluation and selection. Keep in mind that scikit-learn is a generic ML...
