Machine Learning for Time Series Forecasting with Python

CHAPTER 4Introduction to Autoregressive and Automated Methods for Time Series Forecasting

Building forecasts is an integral part of any business, whether it is about revenue, inventory, online sales, or customer demand forecasting. Time series forecasting remains so fundamental because there are several problems and related data in the real world that present a time dimension.

Applying machine learning models to accelerate forecasts enables scalability, performance, and accuracy of intelligent solutions that can improve business operations. However, building machine learning models is often time consuming and complex with many factors to consider, such as iterating through algorithms, tuning machine learning hyperparameters, and applying feature engineering techniques. These options multiply with time series data as data scientists need to consider additional factors, such as trends, seasonality, holidays, and external economic variables.

In this chapter, you will discover a suite of classical methods for time series forecasting that you can test on your forecasting problems. The following paragraphs are structured to give you just enough information on each method to get started with a working code example and where to look to get more information on the method.

Specifically, we will look at the following classical methods:

• Autoregression – This time series technique assumes that future observations at next time stamp are related to the observations at prior time stamps ...