How to predict a time series using Multi Layer Perceptron in Keras

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A time series is a set of data points collected at regular intervals of time, such as stock prices, weather data, or electricity consumption. Predicting a time series using Multi Layer Perceptron (MLP) in Keras involves using historical data to train a model to make predictions about future values.

The first step in setting up an MLP model for time series prediction using Keras is to prepare the dataset. This involves collecting the time series data and formatting it in a way that can be used to train the model. It is important to ensure that the data is properly scaled and that any missing values are filled in.

Next, we need to preprocess the data. This involves splitting the data into training and test sets. We use the training set to train the model and the test set to evaluate its performance. It may also involve creating lags or differences of the time series data to help the model understand the temporal relationships in the data.

Once the data is preprocessed, we can build the MLP model. Keras provides a built-in function for creating a sequential model, which is the type of model used for MLPs. We can add layers to this model, such as an input layer, hidden layers, and an output layer. The input layer receives the preprocessed time series data, and the hidden layers process the data and extract features. The output layer produces the time series prediction.

After building the model, we need to compile it. This involves specifying the optimizer and loss function that will be used during training. The optimizer is responsible for updating the model’s weights during training, and the loss function is used to measure the model’s performance. Additionally, we also need to specify the metrics we want to use to evaluate the model’s performance, such as mean squared error or mean absolute error.

Once the model is compiled, we can train the model on our dataset. This is done by feeding the model time series data from the training set and adjusting the weights based on the performance of the model. This process is repeated for a set number of iterations, known as epochs, until the model reaches a satisfactory level of performance.

After training the model, we can evaluate its performance on the test set using the metrics we specified earlier. This will give us an idea of how well the model will perform on unseen data.

Finally, we can use the trained model to make predictions on new time series data. This can be done by calling the predict function on the model and passing in the time series we want to predict.

In summary, setting up an MLP model for time series prediction using Keras involves preparing a dataset of time series data, preprocessing the data, building the MLP model, compiling the model, training it on the dataset, evaluating its performance on the test set, and making predictions with new time series data. The model is composed by an input layer, hidden layers and an output layer, which are the key elements of the model. The hidden layers process the data and extract features, and the output layer produces the time series predictions. MLP are versatile models that can be applied to a wide range of problems and it’s a good starting point for time series prediction.

 

In this Applied Machine Learning & Data Science Recipe (Jupyter Notebook), the reader will find the practical use of applied machine learning and data science in Python programming: How to predict a time series using Multi Layer Perceptron in Keras.

What should I learn from this recipe?

You will learn:

  • How to code a keras and tensorflow model in Python.
  • How to create training and testing dataset using scikit-learn.
  • How to train a tensorflow and keras model.
  • How to predict a time series using Multi Layer Perceptron in Keras.

 

How to predict a time series using Multi Layer Perceptron in Keras:



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