How to Handle Missing Timesteps in Sequence Prediction Problems with Python
Last Updated on August 28, 2020
It is common to have missing observations from sequence data.
Data may be corrupt or unavailable, but it is also possible that your data has variable length sequences by definition. Those sequences with fewer timesteps may be considered to have missing values.
In this tutorial, you will discover how you can handle data with missing values for sequence prediction problems in Python with the Keras deep learning library.
After completing this tutorial, you will know:
- How to remove rows that contain a missing timestep.
- How to mark missing timesteps and force the network to learn their meaning.
- How to mask missing timesteps and exclude them from calculations in the model.
Kick-start your project with my new book Deep Learning for Time Series Forecasting, including step-by-step tutorials and the Python source code files for all examples.
Let’s get started.
A Gentle Introduction to Linear Algebra
Photo by Steve Corey, some rights reserved.
Overview
This section is divided into 3 parts; they are:
- Echo Sequence Prediction
To finish reading, please visit source site