Journal ArticleDOI
Time series forecasting of petroleum production using deep LSTM recurrent networks
TLDR
A deep learning approach capable to address the limitations of traditional forecasting approaches and show accurate predictions is proposed, and the empirical results show that the proposed DLSTM model outperforms other standard approaches.About:
This article is published in Neurocomputing.The article was published on 2019-01-05. It has received 471 citations till now. The article focuses on the topics: Deep learning & Time series.read more
Citations
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Journal ArticleDOI
Digital Twin: Values, Challenges and Enablers From a Modeling Perspective
TL;DR: This work reviews the recent status of methodologies and techniques related to the construction of digital twins mostly from a modeling perspective to provide a detailed coverage of the current challenges and enabling technologies along with recommendations and reflections for various stakeholders.
Journal ArticleDOI
A review on the long short-term memory model
TL;DR: A comprehensive review of LSTM’s formulation and training, relevant applications reported in the literature and code resources implementing this model for a toy example are presented.
Journal ArticleDOI
An optimized model using LSTM network for demand forecasting
TL;DR: The proposed method automatically selects the best forecasting model by considering different combinations of LSTM hyperparameters for a given time series using the grid search method, which has the ability to capture nonlinear patterns in time seriesData, while considering the inherent characteristics of non-stationary time series data.
Journal ArticleDOI
Probabilistic forecasting with temporal convolutional neural network
TL;DR: In this paper, a probabilistic forecasting framework based on convolutional neural network (CNN) for multiple related time series forecasting is presented, which can be applied to estimate probability density under both parametric and non-parametric settings.
Journal ArticleDOI
Unsupervised Pre-training of a Deep LSTM-based Stacked Autoencoder for Multivariate Time Series Forecasting Problems.
Alaa Sagheer,Mostafa Kotb +1 more
TL;DR: Experimental results clearly show that the unsupervised pre-training approach improves the performance of deep LSTM and leads to better and faster convergence than other models.
References
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Journal ArticleDOI
Long short-term memory
TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.
Journal ArticleDOI
Deep learning
TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
Book
Deep Learning
TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
Journal ArticleDOI
Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation
TL;DR: In this article, a new class of stochastic processes called autoregressive conditional heteroscedastic (ARCH) processes are introduced, which are mean zero, serially uncorrelated processes with nonconstant variances conditional on the past, but constant unconditional variances.
Book ChapterDOI
Time Series Analysis
TL;DR: This paper provides a concise overview of time series analysis in the time and frequency domains with lots of references for further reading.