Journal of Medical Internet ResearchBackgroundAccurate prediction of population-wide depression incidence is vital for effective public mental health management. However, this incidence is often influenced by socioeconomic factors, such as abrupt events or changes ...
Deliang Yang +14 more
doaj +5 more sources
Application of one-, three-, and seven-day forecasts during early onset on the COVID-19 epidemic dataset using moving average, autoregressive, autoregressive moving average, autoregressive integrated moving average, and naïve forecasting methods [PDF]
Data in Brief, 2021 The coronavirus disease 2019 (COVID-19) spread rapidly across the world since its appearance in December 2019. This data set creates one-, three-, and seven-day forecasts of the COVID-19 pandemic's cumulative case counts at the county, health district ...
Christopher J. Lynch, Ross Gore
doaj +2 more sources
Adaptive Online Learning for the Autoregressive Integrated Moving Average Models [PDF]
Mathematics, 2021 This paper addresses the problem of predicting time series data using the autoregressive integrated moving average (ARIMA) model in an online manner. Existing algorithms require model selection, which is time consuming and unsuitable for the setting of ...
Weijia Shao +3 more
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Comparative analysis of Gated Recurrent Units (GRU), long Short-Term memory (LSTM) cells, autoregressive Integrated moving average (ARIMA), seasonal autoregressive Integrated moving average (SARIMA) for forecasting COVID-19 trends [PDF]
Alexandria Engineering Journal, 2022 Several machine learning and deep learning models were reported in the literature to forecast COVID-19 but there is no comprehensive report on the comparison between statistical models and deep learning models. The present work reports a comparative time-
K.E. ArunKumar +4 more
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Predicting the unemployment rate using autoregressive integrated moving average
Cogent Business & ManagementThe objective of this study is to predict unemployment in Indonesia in the wake of the demographic dividend. The sample used in this study is the unemployment data from 1990 to 2022 from the Indonesian Central Bureau of Statistics database.
Andrian Dolfriandra Huruta
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Computational Aspects of Maximum Likelihood Estimation of Autoregressive Fractionally Integrated Moving Average Models [PDF]
Computational Statistics & Data Analysis, 2003 We discuss computational aspects of likelihood-based estimation of univariate ARFIMA (p,d,q) models. We show how efficient computation and simulation is feasible, even for large samples.
Jurgen A. Doornik, Marius Ooms
core +5 more sources
Malaria amidst the COVID-19 pandemic in Gabon: an application of autoregressive integrated moving average (ARIMA) models within an interrupted time series (ITS) framework to hospital-based data. [PDF]
Int J EpidemiolRoeder F +7 more
europepmc +2 more sources
Journal of Family and Community Medicine, 2019 BACKGROUND: The Infant Mortality Rate (IMR) reflects the socioeconomic development of a nation. The IMR was reduced by 28% between 2015 and 2016 (National Family Health Survey-4 [NFHS-4]) as compared to 2005–2006 (NFHS-3), from 57/1000 to 41/1000 live ...
Amit K Mishra +2 more
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A data-driven approach to predict hydrometeorological variability and fluctuations in lake water levels [PDF]
Journal of Water and Land Development, 2023 Beyşehir Lake is the largest freshwater lake in the Mediterranean region of Turkey that is used for drinking and irrigation purposes. The aim of this paper is to examine the potential for data-driven methods to predict long-term lake levels.
Remziye I. Tan Kesgin +4 more
doaj +1 more source
Analyzing the impact of a state concussion law using an autoregressive integrated moving average intervention analysis. [PDF]
BMC Health Serv Res, 2020 Huang L, Sullivan L, Yang J.
europepmc +3 more sources