EconPapers    
Economics at your fingertips  
 

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

Anthony Mouraud ()
Additional contact information
Anthony Mouraud: CEA, France

Entrepreneurship and Sustainability Issues, 2017, vol. 4, issue 3, 282-293

Abstract: Growing interest in meaningful indicators extraction from the huge amounts of data generated by energy efficient buildings instrumentations has led to focusing on so called smart analysis algorithms. This work proposes to focus on statistical and machine learning approaches that make use only of available data to learn relationships, correlations and dependencies between signals. In particular, time series forecasting is a key indication to anticipate, prevent and detect anomalies or unexpected behaviors. We propose to compare performances of a classical Auto Regressive Moving Average (ARMA) approach to a Deep Highway Network on time serie forecasting only making use of past values of the serie. In recent years, Deep Learning has been extensively used for many classification or detection tasks. The complexity of such models is often an argument to discard such approaches for time serie prediction with regard to more common approaches performances. Here we give a first attempt to evaluate benefits of one of the most up to date Deep Learning model in the literature for time serie prediction.

Keywords: sustainability; buildings; time series forecasting; Auto Regressive Moving Average (ARMA); deep networks (search for similar items in EconPapers)
JEL-codes: C45 C53 (search for similar items in EconPapers)
Date: 2017
References: View complete reference list from CitEc
Citations View citations in EconPapers (1) Track citations by RSS feed

Downloads: (external link)
https://jssidoi.org/jesi/uploads/articles/15/Moura ... vs_deep_networks.pdf (application/pdf)
https://jssidoi.org/jesi/article/105 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ssi:jouesi:v:4:y:2017:i:3:p:282-293

Access Statistics for this article

Entrepreneurship and Sustainability Issues is currently edited by Manuela Tvaronaviciene

More articles in Entrepreneurship and Sustainability Issues from VsI Entrepreneurship and Sustainability Center
Bibliographic data for series maintained by Manuela Tvaronaviciene ().

 
Page updated 2018-07-28
Handle: RePEc:ssi:jouesi:v:4:y:2017:i:3:p:282-293