12 Introducing deep learning for time series forecasting
This chapter covers
- When to use deep learning for forecasting
- Exploring the different types of deep learning models
- Getting ready to apply deep learning for time series forecasting
In the last chapter, we concluded the section on time series forecasting using statistical models. These models work particularly well when we have small datasets, usually less than 10 000 data points, and when the seasonal period is monthly, quarterly, or yearly. In situations where we have a daily seasonality or when the dataset is very large, more than 10 000 data points, then our 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 to forecast high-dimensional time series.