Solving Sequence Problems with LSTM in Keras
In this article, you will learn how to perform time series forecasting that is used to solve sequence problems.
Time series forecasting refers to the type of problems where we have to predict an outcome based on time dependent inputs. A typical example of time series data is stock market data where stock prices change with time. Similarly, the hourly temperature of a particular place also changes and can also be considered as time series data. Time series data is basically a sequence of data, hence time series problems are often referred to as sequence problems.
Recurrent Neural Networks (RNN) have been proven to efficiently solve sequence problems. Particularly, Long Short Term Memory Network (LSTM), which is a variation of RNN, is currently being used in a variety of domains to solve sequence problems.
Types of Sequence Problems
Sequence problems can be broadly categorized into the following categories:
- One-to-One: Where there is one input and one output. Typical example of a one-to-one sequence problems is the case where you have an image and you want to predict a single label for the image.
- Many-to-One: In many-to-one sequence problems, we have a sequence