Finally, features correspond to the number of features per time-step. The time-steps is the number of time-steps per sample. The samples are the number of samples in the input data. The input to LSTM layer should be in 3D shape i.e. If you run the above script, you should see the input and output values as shown below: Each output value is 15 times the corresponding input value. Each input consists of one time-step, which in turn contains a single feature. In the script above, we create 20 inputs and 20 outputs. In this next step, we will prepare the dataset that we are going to use for this section. Let's first import the required libraries that we are going to use in this article: from numpy import arrayįrom import one_hotįrom import pad_sequencesįrom import Activation, Dropout, Denseįrom keras.layers import GlobalMaxPooling1Dįrom import Embeddingįrom sklearn.model_selection import train_test_splitįrom import Tokenizerįrom import Concatenate In this section, we will see how to solve one-to-one sequence problem where each time-step has a single feature. One-to-One Sequence Problems with a Single Feature First we will see how to solve one-to-one sequence problems with a single feature and then we will see how to solve one-to-one sequence problems with multiple features. In this section we will see two types of sequence problems. One-to-One Sequence ProblemsĪs I said earlier, in one-to-one sequence problems, there is a single input and a single output. Since, text is also a sequence of words, the knowledge gained in this article can also be used to solve natural language processing tasks such as text classification, language generation, etc. We will be working with Python's Keras library.Īfter reading this article, you will be able solve problems like stock price prediction, weather prediction, etc., based on historic data. In the next part of this series, we will see how to solve one-to-many and many-to-many sequence problems. In this article, we will see how LSTM and its different variants can be used to solve one-to-one and many-to-one sequence problems. Chatbots are also an example of many-to-many sequence problems where a text sequence is an input and another text sequence is the output. For instance, stock prices of 7 days as input and stock prices of next 7 days as outputs. Many-to-Many: Many-to-many sequence problems involve a sequence input and a sequence output.A typical example is an image and its corresponding description. One-to-Many: In one-to-many sequence problems, we have single input and a sequence of outputs.Text classification is a prime example of many-to-one sequence problems where we have an input sequence of words and we want to predict a single output tag. Many-to-One: In many-to-one sequence problems, we have a sequence of data as input and we have to predict a single 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. One-to-One: Where there is one input and one output.Sequence problems can be broadly categorized into the following categories: 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. Recurrent Neural Networks (RNN) have been proven to efficiently solve sequence problems. Time series data is basically a sequence of data, hence time series problems are often referred to as sequence problems. Similarly, the hourly temperature of a particular place also changes and can also be considered as time series data. A typical example of time series data is stock market data where stock prices change with time. Time series forecasting refers to the type of problems where we have to predict an outcome based on time dependent inputs. In this article, you will learn how to perform time series forecasting that is used to solve sequence problems.
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