This repository contains the work of my MSc thesis, a project that spanned over a year, working on a comparative study of CNN and LSTM. The codebase is not refactored yet and will be done soon.
The codebase is not refactored and cleaned yet. To run, execute the
run.py-script. Make sure there is a data folder and corresponding data-files
resides in this folder.
The thesis focus on time-series classification with uni-dimensional CNNs, in which the research question is concerned in answering how this compares to LSTM-based RNNs.
LSTM have complex gating mechanisms, requiring extensive computational resources. Architectures like GRU have been proposed as an alternative, although in recent years, CNNs have proven to be potential competitors to these these architectures.
Moreover, in the field of time-series analysis and time-series classification, comparative studies on neural networks are very limited. Some studies use RNN or CNN separately. Overall, comparisons of RNN and CNN do exist, but moreoften, use cases focus on various language tasks in particular.
The thesis aims to understand how and why CNN can be applicable to time-series classification through extensive experiments across three use cases and datasets, and presents a comparison against LSTM.
- Codebase: ~2000 lines of Python-code
- Most important libraries used: Keras and Pandas
- Use cases in the medical-, energy- and sports domain