Deep Learning with Hybrid Noise Reduction Architecture: Time Series Application
Résumé
Time series forecasting models have fundamental importance to various practical domains. It is desirable that these methods can learn non-linear dependencies and have a high noise resistance. In this paper, we propose a novel architecture using deep learning to address this challenge. We have adapted a new hybrid noise reduction architecture that use recursive error segments for learning and adjusting the predictions. The solution is based on a simultaneous fusion between the outputs of a Convolutional Neural Network (CNN) and a Long Short Term Memory (LSTM) network. This novel model is able to capture di↵erent types of properties which combination can substantially outperform their separate use. Applications involving electricity and financial datasets illustrate the usefulness of the proposed framework.
Origine | Fichiers produits par l'(les) auteur(s) |
---|