In case the definition is unclear, let's look at an example: a stock market ticker where we might observe the price of a stock changing over time, such as Alphabet Inc. in the following screenshot, which is an example of time series:

In the next chapter, we will talk about using recurrent neural networks to model language, which is another type of sequence, a sequence of words. Since you're reading this book, you undoubtedly have some intuition on language sequences already.

If you're new to time series, you might be wondering if it would be possible to use a normal multilayer perceptron to solve a time series problem. You most certainly could do that; however, practically, you almost always get better results using recurrent networks. That said, recurrent neural networks have two other advantages for modeling sequences:

• They can learn really long sequences easier than a normal MLP
• They can handle sequences of varying length

Of course, that leaves us with an important question...