Results 21 to 30 of about 1,891,060 (279)
Time Series Segmentation Based on Stationarity Analysis to Improve New Samples Prediction
Sensors, 2021 A wide range of applications based on sequential data, named time series, have become increasingly popular in recent years, mainly those based on the Internet of Things (IoT).
Ricardo Petri Silva +3 more
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Neural Network Ensembles for Time Series Prediction [PDF]
, 2007 Rapidly evolving businesses generate massive amounts of time-stamped data sequences and defy a demand for massively multivariate time series analysis. For such data the predictive engine shifts from the historical auto-regression to modelling complex
Gabrys, Bogdan, Ruta, Dymitr
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Time Series Prediction on Population Dynamics [PDF]
E3S Web of ConferencesPredicting the time series is a challenging topic mainly on the era of big data. In this research, data taken from population dynamics of one dimension of logistic map with various parameters that leading the system into chaos.
Dwipayana I. Made Eka
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GAN-Based Prediction of Time Series [PDF]
, 2021 The study aims at generating initial and directional insights in the applicability of conditional recurrent generative adversarial nets for the imputation and forecasting of medical time series data. Our experiment with blood pressure series showed that a generative recurrent autoencoder exhibits significant individual learning progress but needs ...
Sven, Festag, Cord, Spreckelsen
openaire +2 more sources
Optimal model-free prediction from multivariate time series [PDF]
, 2015 Forecasting a time series from multivariate predictors constitutes a challenging problem, especially using model-free approaches. Most techniques, such as nearest-neighbor prediction, quickly suffer from the curse of dimensionality and overfitting for ...
Donner, Reik V. +2 more
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Linear Prediction of Long-Range Dependent Time Series [PDF]
, 2007 We present two approaches for next step linear prediction of long memory time series. The first is based on the truncation of the Wiener-Kolmogorov predictor by restricting the observations to the last $k$ terms, which are the only available values in ...
Godet, Fanny
core +8 more sources
Estimation error for blind Gaussian time series prediction [PDF]
, 2011 We tackle the issue of the blind prediction of a Gaussian time series. For this, we construct a projection operator build by plugging an empirical covariance estimation into a Schur complement decomposition of the projector. This operator is then used to
Espinasse, Thibault +2 more
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Time series prediction using artificial neural networks
Ciencias Marinas, 2002 In this work, artificial neural network (ANN) algorithms were used to predict time series of the oceanographic variables Southern Oscilation Index (SOI) and sea surface temperature anomaly (SSTA).
MA Pérez-Chavarría +2 more
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Multi-step learning rule for recurrent neural models: an application to time series forecasting [PDF]
, 2001 Multi-step prediction is a difficult task that has attracted increasing interest in recent years. It tries to achieve predictions several steps ahead into the future starting from current information.
Galván, Inés M., Isasi, Pedro
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Fourier Graph Convolution Network for Time Series Prediction
Mathematics, 2023 The spatio-temporal pattern recognition of time series data is critical to developing intelligent transportation systems. Traffic flow data are time series that exhibit patterns of periodicity and volatility.
Lyuchao Liao +3 more
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