TY - GEN
T1 - Time series forecasting
T2 - 7th IFAC Conference on Manufacturing Modelling, Management, and Control, MIM 2013
AU - Sheremetov, Leonid B.
AU - González-Sánchez, Arturo
AU - López-Yáñez, Itzamá
AU - Ponomarev, Andrew V.
N1 - Funding Information:
The authors would like to thank the CONACYT-SENER project 146515 for partial support of this work. Some parts of the research were carried out under projects funded by grants #13-07-00039 and #13-07-00271 of the Russian Foundation for Basic Research, project #213 of the research program "Intelligent information technologies, mathematical modeling, system analysis and automation" of the Russian Academy of Sciences.
PY - 2013
Y1 - 2013
N2 - This paper describes different models which are used for forecasting in the time series context of petroleum engineering. The objective is to reproduce and further predict future oil production in different scenarios in an adjustable time window. Such time series are very similar to those from the sequential manufacturing processes which are usual in many areas of manufacturing industries. We mainly focus on a feedforward neural network model and a Gamma classifier and compare them both on a benchmark and real industrial data under univariate and multivariate settings. While the former model has become recently a standard tool for modeling and prediction, time series forecasting is not the kind of tasks envisioned while designing and developing the Gamma model. The Gamma classifier is inspired on the Alpha-Beta associative memories, taking the alpha and beta operators as basis for the gamma operator. As experimental results show, pattern recognition based classifier shows very competitive performance. The advantages and limitations of each model are discussed.
AB - This paper describes different models which are used for forecasting in the time series context of petroleum engineering. The objective is to reproduce and further predict future oil production in different scenarios in an adjustable time window. Such time series are very similar to those from the sequential manufacturing processes which are usual in many areas of manufacturing industries. We mainly focus on a feedforward neural network model and a Gamma classifier and compare them both on a benchmark and real industrial data under univariate and multivariate settings. While the former model has become recently a standard tool for modeling and prediction, time series forecasting is not the kind of tasks envisioned while designing and developing the Gamma model. The Gamma classifier is inspired on the Alpha-Beta associative memories, taking the alpha and beta operators as basis for the gamma operator. As experimental results show, pattern recognition based classifier shows very competitive performance. The advantages and limitations of each model are discussed.
KW - Artificial intelligence
KW - Neural networks
KW - Prediction methods
KW - Supply chain
KW - Time-series analysis
UR - http://www.scopus.com/inward/record.url?scp=84884316425&partnerID=8YFLogxK
U2 - 10.3182/20130619-3-RU-3018.00526
DO - 10.3182/20130619-3-RU-3018.00526
M3 - Contribución a la conferencia
AN - SCOPUS:84884316425
SN - 9783902823359
T3 - IFAC Proceedings Volumes (IFAC-PapersOnline)
SP - 957
EP - 962
BT - 7th IFAC Conference on Manufacturing Modelling, Management, and Control, MIM 2013 - Proceedings
PB - IFAC Secretariat
Y2 - 19 June 2013 through 21 June 2013
ER -