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time series models  (Nonlinear Dynamics)

 
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    Nonlinear Dynamics time series models
    Long-horizon tourism forecasts for 2024–2034. The future input features were generated using a <t>univariate</t> <t>time-series</t> model (Prophet), and the projected tourist numbers were predicted using the VotingR2, Gradient Boosting (GB), Random Forest (RF), and HistGradientBoosting (HGB) models.
    Time Series Models, supplied by Nonlinear Dynamics, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/time series models/product/Nonlinear Dynamics
    Average 86 stars, based on 1 article reviews
    time series models - by Bioz Stars, 2026-06
    86/100 stars

    Images

    1) Product Images from "Predicting tourism growth in Saudi Arabia with machine learning models for vision 2030 perspective"

    Article Title: Predicting tourism growth in Saudi Arabia with machine learning models for vision 2030 perspective

    Journal: Scientific Reports

    doi: 10.1038/s41598-025-32509-6

    Long-horizon tourism forecasts for 2024–2034. The future input features were generated using a univariate time-series model (Prophet), and the projected tourist numbers were predicted using the VotingR2, Gradient Boosting (GB), Random Forest (RF), and HistGradientBoosting (HGB) models.
    Figure Legend Snippet: Long-horizon tourism forecasts for 2024–2034. The future input features were generated using a univariate time-series model (Prophet), and the projected tourist numbers were predicted using the VotingR2, Gradient Boosting (GB), Random Forest (RF), and HistGradientBoosting (HGB) models.

    Techniques Used: Generated



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    Image Search Results


    Long-horizon tourism forecasts for 2024–2034. The future input features were generated using a univariate time-series model (Prophet), and the projected tourist numbers were predicted using the VotingR2, Gradient Boosting (GB), Random Forest (RF), and HistGradientBoosting (HGB) models.

    Journal: Scientific Reports

    Article Title: Predicting tourism growth in Saudi Arabia with machine learning models for vision 2030 perspective

    doi: 10.1038/s41598-025-32509-6

    Figure Lengend Snippet: Long-horizon tourism forecasts for 2024–2034. The future input features were generated using a univariate time-series model (Prophet), and the projected tourist numbers were predicted using the VotingR2, Gradient Boosting (GB), Random Forest (RF), and HistGradientBoosting (HGB) models.

    Article Snippet: More broadly, existing works on the Kingdom’s tourism sector tend to rely on traditional econometric or time-series models (e.g., ARIMA, gravity models) , , which inadequately capture the nonlinear dynamics of modern tourism patterns shaped by Vision 2030, mega-events, and religious travel cycles.

    Techniques: Generated

    Comparison of 12-month and 33-month ahead forecasts for monthly Hepatitis B incidence in Henan Province, China, 2014–2023, using seasonal autoregressive integrated moving average, seasonal autoregressive integrated moving average with exogenous variables, Bayesian structural time series without Baidu Index, and Bayesian structural time series with Baidu Index models. A Seasonal autoregressive integrated moving average model for 12-month ahead forecasts. B Seasonal autoregressive integrated moving average with exogenous variables model for 12-month ahead forecasts. C Bayesian structural time series model without Baidu Index for 12-month ahead forecasts. D Bayesian structural time series model with Baidu Index for 12-month ahead forecasts. E Seasonal autoregressive integrated moving average model for 33-month ahead forecasts. F Seasonal autoregressive integrated moving average with exogenous variables model for 33-month ahead forecasts. G Bayesian structural time series model without Baidu Index for 33-month ahead forecasts. H Bayesian structural time series model with Baidu Index for 33-month ahead forecasts. The Bayesian structural time series models, especially with Baidu Index, demonstrate superior predictive performance

    Journal: Archives of Public Health

    Article Title: Early warning of hepatitis B epidemics in Henan Province, China, from 2014 to 2023 based on Baidu Index and Bayesian Structural Time Series model

    doi: 10.1186/s13690-026-01837-y

    Figure Lengend Snippet: Comparison of 12-month and 33-month ahead forecasts for monthly Hepatitis B incidence in Henan Province, China, 2014–2023, using seasonal autoregressive integrated moving average, seasonal autoregressive integrated moving average with exogenous variables, Bayesian structural time series without Baidu Index, and Bayesian structural time series with Baidu Index models. A Seasonal autoregressive integrated moving average model for 12-month ahead forecasts. B Seasonal autoregressive integrated moving average with exogenous variables model for 12-month ahead forecasts. C Bayesian structural time series model without Baidu Index for 12-month ahead forecasts. D Bayesian structural time series model with Baidu Index for 12-month ahead forecasts. E Seasonal autoregressive integrated moving average model for 33-month ahead forecasts. F Seasonal autoregressive integrated moving average with exogenous variables model for 33-month ahead forecasts. G Bayesian structural time series model without Baidu Index for 33-month ahead forecasts. H Bayesian structural time series model with Baidu Index for 33-month ahead forecasts. The Bayesian structural time series models, especially with Baidu Index, demonstrate superior predictive performance

    Article Snippet: G Bayesian structural time series model without Baidu Index for 33-month ahead forecasts.

    Techniques: Comparison