Results 241 to 250 of about 153,678 (301)

A comparative study of time series foundation models for hand, foot, and mouth disease forecasting: TimesFM, Moirai, and traditional approaches. [PDF]

Front Public Health
Wang Y, Huang G, Chen C, Li Q, Xu X.
europepmc   +1 more source

Prediction and Performance of BDS Satellite Clock Bias Based on CNN-LSTM-Attention Model. [PDF]

Sensors (Basel)
Ma J, Tang J, Teng H, Wu X.
europepmc   +1 more source

Global, regional, and national burdens of non-rheumatic calcific aortic valve disease in middle-aged and elderly populations from 1990 to 2021 and predictions up to 2050: trends, predictions, and health inequity. [PDF]

Eur J Med Res
Hu J, Ge T, Zhang M, He K, Li G, Zou R, Fan X.   +6 more
europepmc   +1 more source

Building and Prospectively Evaluating a Prediction Model to Forecast Urgent Dialysis Needs across Four Tertiary Hospitals.

Am J Nephrol
Lim B, Zhu K, Zorcic K, Chan CTM, Fralick M.   +4 more
europepmc   +1 more source

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ARIMA model's superiority over f-ARIMA model

WCC 2000 - ICCT 2000. 2000 International Conference on Communication Technology Proceedings (Cat. No.00EX420), 2002
We make it clear that the SRD model is better than the LRD model with time-scale resolution over 60 seconds. The conclusion was derived in the following way. We used the real traffic data observed at our university's router, which is the gateway to the Internet for approximately 1000 machines.
Y. Takahashi, H. Aida, T. Saito
openaire   +1 more source

ARIMA Processes With ARIMA Parameters

Journal of Business & Economic Statistics, 1993
This article introduces a general class of nonlinear and nonstationary time series models whose basic scheme is an autoregressive integrated moving average (ARIMA). The main feature is that the parameters are assumed to behave like a vector ARIMAx model in which the exogenous (x) component is represented by the regressors of the observable process. For
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Forecasting stock index returns using ARIMA-SVM, ARIMA-ANN, and ARIMA-random forest hybrid models

International Journal of Banking, Accounting and Finance, 2014
The purpose of this paper is to develop and identify the best hybrid model to predict stock index returns. We develop three different hybrid models combining linear ARIMA and non-linear models such as support vector machines (SVM), artificial neural network (ANN) and random forest (RF) models to predict the stock index returns. The performance of ARIMA-
Manish Kumar, M. Thenmozhi
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???????????????????????? ???????????? ARIMA ?????? ?????????????????????????? ?????????????? ?????????????????? ?????????????? ??????????????

2013
Forecast of profitable part of budget of Ukraine in 2011 is built.
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