Gaussian process for nonstationary time series prediction

By Sofiane Brahim-Belhouari, Amine Bermak. 2004

301 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

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Citations in the corpus

Deep Factors for Forecasting (2019, background): “Gaussian Processes (Rasmussen & Williams, 2006; Seeger, 2004) (GPs) are another family of the models that have been applied to time series forecasting (Girard et al., 2003; Brahim-Belhouari & Bermak, 2004).”
Deep Learning for Time Series Forecasting: Tutorial and Literature Survey (2020, methodology): “, via a Kalman filter, and Gaussian Processes (GPs) [149, 159, 67, 27].”
Time series forecasting for nonlinear and non-stationary processes: a review and comparative study (2015, background): “For instance, non-stationary covariance functions introduced to overcome the stationarity assumptions (Brahim-Belhouari and Bermak, 2004; Plagemann et al., 2008; Wang et al, 2008) are suited only for simple nonlinear and non-stationary forms, such as linear trends, and introduce additional…”
Practical options for selecting data-driven or physics-based prognostics algorithms with reviews (2015, methodology): “Belhouari and Bermak [85] employed non-stationary GP to predict respiration signals and compared them with those from an exponential covariance function.”
Forecast combinations of computational intelligence and linear models for the NN5 time series forecasting competition (2011, methodology): “Among the few other studies on using Gaussian process regression for time series forecasting are the works of Brahim-Belhouari and Bermak (2004), Chapados and Bengio (2007), and Girard, Rasmussen, Quinonero-Candela, and Murray-Smith (2003).”
A Review of Deep Learning Models for Time Series Prediction (2019, methodology): “Considering the non-stationarity of a time series, a covariance matrix in a GP was designed in [137].”
Diabetes Prediction Using Ensembling of Different Machine Learning Classifiers (2020, methodology): “…the Linear Discriminant Analysis (LDA) [8], Quadratic Discriminant Analysis (QDA) [9], Naive Bayes (NB) [10], Gaussian Process Classiﬁcation (GPC) [11], Support Vector Machine (SVM) [12], Artiﬁcial Neural Network (ANN) [13], AdaBoost (AB) [14], Logistic Regression (LR) [15], Decision Tree (DT)…”
A Survey of Methods for Time Series Change Point Detection (2016, unknown): “A GP is a generalization of a Gaussian distribution and is defined as a collection of random variables, any finite number of which have a joint Gaussian distribution [56][57].”