Results 31 to 40 of about 50,820 (258)
Dense Sampling of Time Series for Forecasting
IEEE Access, 2022 A time series contain a large amount of information suitable for forecasting. Classical statistical and recent deep learning models have been widely used in a variety of forecasting applications.
Il-Seok Oh, Jin-Seon Lee
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Pediatric Blood &Cancer, EarlyView.ABSTRACT Hemophilic arthropathy remains the leading morbidity in hemophilia despite modern prophylaxis, and early joint damage may be missed by routine exams. This study explored T2* MRI as a noninvasive biomarker of hemosiderin deposition in pediatric hemophilia.
Jessica Garcia +6 more
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DTMamba : Dual Twin Mamba for Time Series Forecasting
Tsinghua Science and TechnologyLong-term Time Series Forecasting (LTSF) has always been an important task where models need to effectively capture hidden patterns in the time series in order to make accurate predictions about future states.
Zexue Wu +3 more
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TIME SERIES FORECASTING BY THE ARIMA METHOD
Scientific Journal of Astana IT University, 2022 The variety of communication services and the growing number of different sensors with the appearance of IoT (Internet of Things) technology generate significantly different types of network traffic.
Gulnara Bektemyssova +3 more
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, 2015 In this chapter four combinations of input features and the feedforward, cascade forward and recurrent architectures are compared for the task of forecast tourism time series. The input features of the ANNs consist in the combination of the previous 12 months, the index time modeled by two nodes used to the year and month and one input with the daily ...
Teixeira, João Paulo +1 more
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Pediatric Blood &Cancer, EarlyView.ABSTRACT Purpose Despite 5‐year survival rates of over 90% among children and adolescents/young adults (CAYAs) with classic Hodgkin lymphoma (cHL), 15%–20% relapse after frontline therapy. Prior analysis of frontline Children's Oncology Group (COG) clinical trials demonstrated that, despite similar rates of relapse, non‐Hispanic Black (NHB) and ...
Mallorie B. Heneghan +14 more
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Forecasting for Stationary Binary Time Series
Acta Applicandae Mathematica, 2003 The forecasting problem for a stationary and ergodic binary time series $\{X_n\}_{n=0}^{\infty}$ is to estimate the probability that $X_{n+1}=1$ based on the observations $X_i$, $0\le i\le n$ without prior knowledge of the distribution of the process $\{X_n\}$. It is known that this is not possible if one estimates at all values of $n$.
Gusztáv Morvai, Benjamin Weiss 0002
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Pediatric Blood &Cancer, EarlyView.ABSTRACT Introduction Anthracycline‐related cardiac remodeling precedes heart failure in childhood cancer survivors. The objectives of this study were to determine the relationships between patient‐specific factors, moderate‐to‐vigorous physical activity (MVPA), and cardiac remodeling.
Hari K. Narayan +15 more
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Iterative Forecasting of Short Time Series
Applied SciencesWe forecast short time series iteratively using a model based on stochastic differential equations. The recorded process is assumed to be consistent with an α-stable Lévy motion.
Evangelos Bakalis
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TIME SERIES FORECASTING USING NEURAL NETWORKS [PDF]
Challenges of the Knowledge Society, 2013 Recent studies have shown the classification and prediction power of the Neural Networks. It has been demonstrated that a NN can approximate any continuous function.
BOGDAN OANCEA, ŞTEFAN CRISTIAN CIUCU
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