Gwill / Time-series---deep-learning---state-of-the-art

Scientific time series and deep learning state of the art

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Deep Learning and Time Series

This document shows a list of bibliographical references on DeepLearning and Time Series, organized by type and year. I add some additional notes on each reference.

Table of contents

Deef Belief Network with Restricted Boltzmann Machine

Journal

2017

2016

2014

Conference

2017

  • Norbert Agana; Abdollah Homaifar (2017). A deep learning based approach for long-term drought prediction. SoutheastCon

    Summary: The paper looks into the drought prediction problem using deep learning algorithms. They propose a Deep Belief Network consisting of two Restricted Boltzmann Machines. The study compares the efficiency of the proposed model to that of traditional approaches such as Multilayer Perceptron (MLP) and Support Vector Regression (SVR) for predicting the different time scale drought conditions.

    Notes:

    • Model train -> unsupervised learning
    • Model Fine-tuning connection weights -> Back-propagation

2016

2015

Long short-term memory

Journal

2018

2017

Conference

2018

2017

2016

Auto-Encoders

Journal

2017

2016

Conference

2016

2015

2013

Combination of the above

Journal

2018

2017

Conference

2017

2016

Others

2018

2017

2016

2015

2014

Reviews

2017

2014

2012

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Scientific time series and deep learning state of the art