Machine Learning for Time Series Forecasting with Python

CHAPTER 5Introduction to Neural Networks for Time Series Forecasting

As we discussed in the previous chapters, the ability to accurately forecast a sequence into the future is critical in many industries: finance, supply chain, and manufacturing are just a few examples. Classical time series techniques have served this task for decades, but now deep learning methods—similar to those used in computer vision and automatic translation—have the potential to revolutionize time series forecasting as well.

Due to their applicability to many real-life problems, such as fraud detection, spam email filtering, finance, and medical diagnosis, and their ability to produce actionable results, deep learning neural networks have gained a lot of attention in recent years. Generally, deep learning methods have been developed and applied to univariate time series forecasting scenarios, where the time series consists of single observations recorded sequentially over equal time increments (Lazzeri 2020).

For this reason, they have often performed worse than naïve and classical forecasting methods, such as autoregressive integrated moving average (ARIMA). This has led to a general misconception that deep learning models are inefficient in time series forecasting scenarios, and many data scientists wonder whether it's really necessary to add another class of methods, like convolutional neural networks or recurrent neural networks, to their time-series toolkit (Lazzeri 2020).

In this chapter, we ...