Results 11 to 20 of about 10,065 (268)
Regime-Switching Discrete ARMA Models for Categorical Time Series [PDF]
Entropy, 2020 For the modeling of categorical time series, both nominal or ordinal time series, an extension of the basic discrete autoregressive moving-average (ARMA) models is proposed.
Christian H. Weiß
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Revisiting inference for ARMA models: Improved fits and superior confidence intervals. [PDF]
PLoS ONEAutoregressive moving average (ARMA) models are widely used for analyzing time series data. However, standard likelihood-based inference methodology for ARMA models has avoidable limitations.
Jesse Wheeler, Edward L Ionides
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Hybrid models combining trend and seasonality components with machine learning algorithms provide accurate forecasting of malaria incidence. [PDF]
PLOS Global Public HealthForecasting malaria incidence is vital for effective resource allocation during malaria elimination. In this study, we highlight robust models for forecasting incidence using climatic and malaria data from Goa, India.
Syed Shah Areeb Hussain +8 more
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Are We in Control? How Best to Include a Control Group in Interrupted Time Series Designs: A Simulation Study [PDF]
Journal of Evaluation in Clinical Practice, Volume 32, Issue 4, June 2026.ABSTRACT Background While controlled interrupted time series (CITS) are commonly used to evaluate public health policies, how to incorporate control(s) into their statistical modelling has received limited attention. We aimed to compare the statistical performance of different model formulations for including control groups in various segmented ...
Francesco Manca +2 more
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Bayesian analysis of ARMA models [PDF]
, 2000 Root cancellation in Auto Regressive Moving Average (ARMA) models leads tolocal non-identification of parameters. When we use diffuse or normal priorson the parameters of the ARMA model, posteriors in Bayesian analyzes show ana posteriori favor for this local non-identification. We show that the priorand posterior of the parameters of an ARMA model are
Kleibergen, F.R., Hoek, H.-
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SchWARMA: A model-based approach for time-correlated noise in quantum circuits
Physical Review Research, 2021 Temporal noise correlations are ubiquitous in quantum systems, yet often neglected in the analysis of quantum circuits due to the complexity required to accurately characterize and model them.
Kevin Schultz +3 more
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Bayesian Inference for Seasonal ARMA Models [PDF]
The Egyptian Statistical Journal, 1987 An essential ingredient of any time series anatysis is the estimation of the modcl parameters. The main objective of this paper is to develop a convenient Rayesian technique for estimation which can be used to analyze ‘seasonal autoregressive moving ...
Samir Shaarawy, Mohamed Ismail
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Groundwater Depth Forecasting Using a Coupled Model
Discrete Dynamics in Nature and Society, 2021 Accurate and reliable prediction of groundwater depth is a critical component in water resources management. In this paper, a new method based on coupling wavelet decomposition method (WA), autoregressive moving average (ARMA) model, and BP neural ...
Manfei Zhang +3 more
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Mathematics, 2020 In this paper, we compare the predictions on the market liquidity in crypto and fiat currencies between two traditional time series methods, the autoregressive moving average (ARMA) and the generalized autoregressive conditional heteroskedasticity (GARCH)
Klender Cortez +2 more
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Modelling The Volatility of Frankfurt Stock Exchange (DAX) Returns Using hybrid Models [PDF]
Financial Markets, Institutions and RisksRecently, the interest of researchers in the use of hybrid models in the process of analyzing model time series with fluctuations and forecasting fluctuations in financial time series has increased significantly. Hybrid ARMA-GARCH models were created for
Hadj Khelifa +2 more
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