Results 251 to 260 of about 93,376 (303)

Time series forecasting of infant mortality rate in India using Bayesian ARIMA models. [PDF]

BMC Public Health
Singh A, Tripathi T, Ranjan R, Tiwari AK.   +3 more
europepmc   +1 more source

A novel LLM time series forecasting method based on integer-decimal decomposition. [PDF]

Sci Rep
Wang L, Dong K, Zhao X.
europepmc   +1 more source

Novel Time-Series Forecasting Method to Enhance Accuracy of Real-Time EEG Detection for BCI-Based Neurofeedback Motor Training in Individuals with Cerebral Palsy and Other Neurological Disorders. [PDF]

Bioengineering (Basel)
Gravunder A, Studnicki A, Kline J, Behboodi A, Bulea TC, Damiano DL.   +5 more
europepmc   +1 more source

Consistency regularization for few shot multivariate time series forecasting. [PDF]

Sci Rep
She Y, Hong Y, Shen S, Yang B, Zhang L, Wang J.   +5 more
europepmc   +1 more source

Time-Series Forecasting Method Based on Hierarchical Spatio-Temporal Attention Mechanism. [PDF]

Sensors (Basel)
Xiao Z, Liu J, Cao X, Wang K, Li D, Liu Q.   +5 more
europepmc   +1 more source

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Forecasting Trends in Time Series

Management Science, 1985
Most time series methods assume that any trend will continue unabated, regardless of the forecast lead time. But recent empirical findings suggest that forecast accuracy can be improved by either damping or ignoring altogether trends which have a low probability of persistence.
Everette S. Gardner, Jr., Ed. Mckenzie
openaire   +2 more sources

Time Series Forecasting as a Measure

International Journal of Advanced Pervasive and Ubiquitous Computing, 2013
In this paper, the time series prediction is as a measure. At the same time, the optimal combination forecast using each method can be defined as the actual impact measurement value of true. Effect of its theoretical estimation has error correlation coefficient values.
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FORECASTING TIME SERIES USING WAVELETS

International Journal of Wavelets, Multiresolution and Information Processing, 2007
This paper deals with wavelets in time series, focusing on statistical forecasting purposes. Recent approaches involve wavelet decompositions in order to handle non-stationary time series in such context. A method, proposed by Renaud et al.,11 estimates directly the prediction equation by direct regression of the process on the Haar non-decimated ...
Mina Aminghafari, Jean-Michel Poggi
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Relational time series forecasting

The Knowledge Engineering Review, 2018
Abstract Networks encode dependencies between entities (people, computers, proteins) and allow us to study phenomena across social, technological, and biological domains. These networks naturally evolve over time by the addition, deletion, and changing of links, nodes, and attributes.
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Ensemble Time Series Forecasting with XCSF

2016 IEEE 10th International Conference on Self-Adaptive and Self-Organizing Systems (SASO), 2016
Time series forecasting constitutes an important aspect of any technical system, since the underlying data generating processes vary over time. In order to take system designers out of the loop, efforts for designing self-adaptive, learning systems have extensively been made. By means of forecasting the succeeding system state, the system is enabled to
Matthias Sommer, Anthony Stein, Jörg Hähner   +2 more
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