TSViz: Demystification of Deep Learning Models for Time-Series Analysis

TSViz: Demystification of Deep Learning Models for Time-Series Analysis

This paper presents a novel framework for the demystification of convolutional deep learning models for time-series analysis. This is a step toward making informed/explainable decisions in the domain of time series, powered by deep learning. There have been numerous efforts to increase the interpret...

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Bibliografiska uppgifter
Huvudupphovsmän: Shoaib Ahmed Siddiqui, Dominique Mercier, Mohsin Munir, Andreas Dengel, Sheraz Ahmed
Materialtyp: Artikel
Språk:English
Publicerad: IEEE 2019-01-01
Serie:IEEE Access
Ämnen:
Deep learning
representation learning
convolutional neural networks
time-series analysis
time-series forecasting
feature importance
Länkar:https://ieeexplore.ieee.org/document/8695734/

Internet

https://ieeexplore.ieee.org/document/8695734/

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