Innovative time series forecasting: auto regressive moving average vs deep networks

Innovative time series forecasting: auto regressive moving average vs deep networks

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Bibliographic Details
Main Author: Anthony Mouraud
Format: article
Language:EN
Published: Entrepreneurship and Sustainability Center 2017
Subjects:
Environmental sciences
GE1-350
Technological innovations. Automation
HD45-45.2
Online Access:https://doaj.org/article/c592b48fad6f49f9881a5d3cc60b59d8
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spelling oai:doaj.org-article:c592b48fad6f49f9881a5d3cc60b59d82021-11-13T17:16:00ZInnovative time series forecasting: auto regressive moving average vs deep networks2345-028210.9770/jesi.2017.4.3S(4)https://doaj.org/article/c592b48fad6f49f9881a5d3cc60b59d82017-03-01T00:00:00Zhttps://jssidoi.org/jesi/article/105https://doaj.org/toc/2345-0282Anthony MouraudEntrepreneurship and Sustainability CenterarticleEnvironmental sciencesGE1-350Technological innovations. AutomationHD45-45.2ENEntrepreneurship and Sustainability Issues, Vol 4, Iss 3, Pp 282-293 (2017)
institution DOAJ
collection DOAJ
language EN
topic Environmental sciences
GE1-350
Technological innovations. Automation
HD45-45.2
spellingShingle Environmental sciences
GE1-350
Technological innovations. Automation
HD45-45.2
Anthony Mouraud
Innovative time series forecasting: auto regressive moving average vs deep networks
format article
author Anthony Mouraud
author_facet Anthony Mouraud
author_sort Anthony Mouraud
title Innovative time series forecasting: auto regressive moving average vs deep networks
title_short Innovative time series forecasting: auto regressive moving average vs deep networks
title_full Innovative time series forecasting: auto regressive moving average vs deep networks
title_fullStr Innovative time series forecasting: auto regressive moving average vs deep networks
title_full_unstemmed Innovative time series forecasting: auto regressive moving average vs deep networks
title_sort innovative time series forecasting: auto regressive moving average vs deep networks
publisher Entrepreneurship and Sustainability Center
publishDate 2017
url https://doaj.org/article/c592b48fad6f49f9881a5d3cc60b59d8
work_keys_str_mv AT anthonymouraud innovativetimeseriesforecastingautoregressivemovingaveragevsdeepnetworks
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