12 Introducing deep learning for time series forecasting

In the last chapter, we concluded the part of the book on time series forecasting using statistical models. Those models work particularly well when you have small datasets (usually less than 10,000 data points), and when the seasonal period is monthly, quarterly, or yearly. In situations where you have daily seasonality or where the dataset is very large (more than 10,000 data points), those statistical models become very slow, and their performance degrades.

Thus, we turn to deep learning. Deep learning is a subset of machine learning that focuses on building models on the neural network architecture. Deep learning has the advantage that it tends to perform better as more data is available, making it a great choice for forecasting high-dimensional time series.

12.1 When to use deep learning for time series forecasting

12.2 Exploring the different types of deep learning models

12.3 Getting ready to apply deep learning for forecasting

12.3.1 Performing data exploration

12.3.2 Feature engineering and data splitting

12.4 Next steps

12.5 Exercise

Summary