An additive model is explored to predict patterns of time series and investigate means of constructing forecast time series models in the future. The main components(trend, periodical, and irregular) of the KIUB(DORIS) and KIT3, TASH, MADK, and MTAL(GNSS) international stations coordinate time...
time series models, a first-order autoregressive (AR(1)) model and two second-order auto-regressive (AR(2)) models, all of which are time-invariant... J Sullivan,WH Woodall - 《Fuzzy Sets & Systems》 被引量: 335发表: 1994年 Evaluation of statistical models for forecast errors from the...
By researching the structure of economic time series using ARIMA model, we firstly establish the expression of trend-cycle component according to the order of integration (d), and set up different forms of structural time series models. In the structure time series model, the economic indicator ...
A new class of Smooth Transition Autoregressive(STAR) models, based on cubic spline type transition functions, has been recently introduced by the authors of this paper and subjected to comparison with models based on the traditional logistic functions. A very high degree of similarity between these...
with limited amplitude more accurately, while the XGBoost and LSTM models can predict multi-step ahead with appropriate data preprocessing, and (c) All the three models can predict the data tendency with model updating over time, but the prediction accuracy of the LSTM model is more favorable. ...
aquality control supervisor for each function 质量管理监督员为每个作用[translate] aThe estimation and application of long memory series models. Journal of Time Series Analysis 4, 221-238. 长的记忆系列模型的估计和应用。 时间序列分析4, 221-238学报。[translate]...
Empirical likelihood for quantile regression models with longitudinal data We develop two empirical likelihood-based inference procedures for longitudinal data under the framework of quantile regression. The proposed methods avoid... HJ Wang,Z Zhu - 《Journal of Statistical Planning & Inference》 被引量...
Forecasting high waters at Venice Lagoon using chaotic time series analisys and nonlinear neural netwoks Examines the application of time series analysis using nonlinear dynamics systems theory and multilayer neural networks models to the time sequence of wate... Zaldívar, J.M.,E Gutiérrez,Galván,...
In the past years, several models had been introduced for stock price prediction. Among them ARIMA models are from statistical viewpoints. They are known for the robustness and efficiency in financial time series forecasting and are extensively used in the research field of economics and finance. ...
<p id="p00005">In the fields of psychology, education, and clinical science, researchers have devoted increasing attention to the intraindividual dynamics of behaviors, minds, and treatment effects over time, making personalized modeling a growing concer