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Saturation in autoregressive models [PDF]
Notas Económicas, 2006 In this paper, we extend the impulse saturation algorithm to a class of dynamic models. We show that the procedure is still correctly sized for stationary AR(1) processes, independently of the number of splits used for sample partitions.
Carlos Santos, David Hendry
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Message Passing-Based Inference for Time-Varying Autoregressive Models [PDF]
Entropy, 2021 Time-varying autoregressive (TVAR) models are widely used for modeling of non-stationary signals. Unfortunately, online joint adaptation of both states and parameters in these models remains a challenge.
Albert Podusenko +2 more
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Entropy Power, Autoregressive Models, and Mutual Information [PDF]
Entropy, 2018 Autoregressive processes play a major role in speech processing (linear prediction), seismic signal processing, biological signal processing, and many other applications.
Jerry Gibson
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Вестник Самарского университета: Естественнонаучная серия, 2023 For modeling in time series, models with fractional differences are widely used. The best known model is the ARFIMA (autoregressive fractionally integrated moving average) model.
Dmitriy V. Ivanov
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Some remarks on selfnormalization for a simple spatial autoregressive model
Lietuvos Matematikos Rinkinys, 2021 In the paper I continue investigations on the self-normalization of simple autoregressive field Xt,s = aXt−1,s + bXt,s−1 + εt,s started in [5]. And extend previous results when the variance of the innovations of the process above are not finite.
Romas Zovė
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Autoregressive graph Volterra models and applications
EURASIP Journal on Advances in Signal Processing, 2023 Graph-based learning and estimation are fundamental problems in various applications involving power, social, and brain networks, to name a few. While learning pair-wise interactions in network data is a well-studied problem, discovering higher-order ...
Qiuling Yang +3 more
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Graph Neural Networks for Modeling Causality in International Trade
Proceedings of the International Florida Artificial Intelligence Research Society Conference, 2021 Neural network algorithms have proven successful for accurate classifications in many domains such as image recognition and semantic parsing. However, they have long suffered from the lack of ability to measure causality, predict outliers effectively, or
Anderson Monken +4 more
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Entropy, 2020 This article describes a refinement of recurrence analysis to determine the delay in the causal influence between a driver and a target, in the presence of additional perturbations affecting the time series of the response observable.
Emmanuele Peluso +2 more
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A Neural Networks Based Method for Multivariate Time-Series Forecasting
IEEE Access, 2021 In recent years, more and more deep neural network methods have been used in the forecasting research of multivariate time series. Comparing to the traditional methods such as autoregressive models, methods based on neural networks have achieved superior
Shaowei Li, He Huang, Wei Lu
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Agronomy, 2023 Crop simulation models are an important tool for assessing agroecosystem performance and the impact of agrotechnologies on soil cover condition.
Islombek Mirpulatov +2 more
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