Multilayer Neural Network with Multi-Valued Neurons in Time Series Forecasting of Oil Production

Abstract

In this paper, we discuss the long-term time series forecasting using a Multilayer Neural Network with Multi-Valued Neurons (MLMVN). This is complex-valued neural network with a derivative-free backpropagation learning algorithm. We evaluate the proposed approach using a real-world data set describing the dynamic behavior of an oilfield asset located in the coastal swamps of the Gulf of Mexico. We show that MLMVN can be efficiently applied to univariate and multivariate multi-step ahead prediction of reservoir dynamics. This paper is not only intended for proposing a novel model of forecasting but to study carefully several aspects of the application of ANN models to time series forecasting that could be of the interest for pattern recognition community.

Modelling wholesale distribution operations: an artificial intelligence framework

“…The methodological approach makes use of an ANN multilayer perceptron, which is trained using an error back-propagation algorithm. Aizenberg et al (2016) developed a multilayer neural network with multi-valued neurons for long-term time series forecasting in the context of oil production.…”

Long-Term Forecasting for World Oil Production

“…Aizenberg et al [7] used Multilayer Neural Network with Multi-Valued Neurons (MLMVN) to conduct log-term forecasting for oil production. They proposed to use a complex-valued neural network for forecasting and studied some important aspects of the application of ANN models.…”

Predict Time Series with Multiple Artificial Neural Networks

“…For is the iterative method, which is doing repeated one-step predictions up to the desired horizontal point [15].…”