Machine Learning for Time Series Forecasting with Python

CHAPTER 1Overview of Time Series Forecasting

Time series is a type of data that measures how things change over time. In a time series data set, the time column does not represent a variable per se: it is actually a primary structure that you can use to order your data set. This primary temporal structure makes time series problems more challenging as data scientists need to apply specific data preprocessing and feature engineering techniques to handle time series data.

However, it also represents a source of additional knowledge that data scientists can use to their advantage: you will learn how to leverage this temporal information to extrapolate insights from your time series data, like trends and seasonality information, to make your time series easier to model and to use it for future strategy and planning operations in several industries. From finance to manufacturing and health care, time series forecasting has always played a major role in unlocking business insights with respect to time.

Following are some examples of problems that time series forecasting can help you solve:

• What are the expected sales volumes of thousands of food groups in different grocery stores next quarter?
• What are the resale values of vehicles after leasing them out for three years?
• What are passenger numbers for each major international airline route and for each class of passenger?
• What is the future electricity load in an energy supply chain infrastructure, so that suppliers can ensure ...