In this blog post, I will discuss the use of deep leaning methods to classify time-series data, without the need to manually engineer features. The example I will consider is the classic Human Activity Recognition (HAR) dataset from the UCI repository. The dataset contains the raw time-series data, as well as a pre-processed one with 561 engineered features. I will compare the performance of typical machine learning algorithms which use engineered features with two deep learning methods (convolutional and recurrent neural networks) and show that deep learning can surpass the performance of the former.
I have used Tensorflow for the implementation and training of the models discussed in this post. In the discussion below, code snippets are provided to explain the implementation. For the complete code, please see my Github repository.
Click through for the samples, or check out the repo, linked above.