LSTM Autoencoder for Series Data

This guide introduces how to use LSTM Autoencoders for reconstructing time series data. An Autoencoder is a specialized neural network that learns to compress and then reconstruct input data. It works by encoding the input into a compact representation known as the latent variable, which captures the essential features of the data while reducing noise. The network then reconstructs the original input from this compressed form.

This example provides a practical approach to implementing LSTM Autoencoders, focusing on how they can be applied to time series data reconstruction. For a more detailed understanding of LSTM Autoencoders and their underlying principles, you can explore various resources, including videos and blogs. This guide will concentrate on the hands-on coding aspects to help you get started.

To Define in basic terms an Autoencoder works in this way it takes an input sample, extracts all the important information (called as a latent variable), which also helps in eliminating noise, and reconstructs back the input at the output with the help of the latent variable.

For additional insights, check out this [step-by-step blog post](https://towardsdatascience.com/step-by-step-understanding-lstm-autoencoder-layers-ffab055b6352).

We will try to recreate the sequence [ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ] using LSTM Autoencoder Structure

With the input shape defined, we can proceed to design our encoder-decoder structure. Simply put, an Autoencoder functions by taking an input sample, extracting its key features (referred to as the latent variable), which aids in noise reduction, and then reconstructing the original input using this compressed representation.

Now its time to Train the model

Here it is important to understand the input and output to the model has to be same sequence so that the model can learn to understand the “MOST” important features of the Input.

Now let us Test the Model

Let us first test the model with same input sequence

The model is able to recreate the input with some error. Let us give a completely new sequence and check the output

The model fails in this case as the model has not been trained with enough data.

Summary

By diving into the practical implementation of LSTM Autoencoders, you’ll gain valuable hands-on experience with data reconstruction techniques. If you have any questions or need further assistance, feel free to explore the resources provided or reach out to the community. Happy coding!

GitHub Repository: https://github.com/adnankarol/LSTM-Autoencoders-Demo