Results 41 to 50 of about 1,891,060 (279)
Bootstraping financial time series [PDF]
, 2002 It is well known that time series of returns are characterized by volatility clustering and excess kurtosis. Therefore, when modelling the dynamic behavior of returns, inference and prediction methods, based on independent and/or Gaussian observations ...
Pascual, Lorenzo, Ruiz Ortega, Esther
core +4 more sources
Deep and Confident Prediction for Time Series at Uber
, 2017 Reliable uncertainty estimation for time series prediction is critical in many fields, including physics, biology, and manufacturing. At Uber, probabilistic time series forecasting is used for robust prediction of number of trips during special events ...
Laptev, Nikolay, Zhu, Lingxue
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On the prediction of stationary functional time series [PDF]
, 2014 This paper addresses the prediction of stationary functional time series. Existing contributions to this problem have largely focused on the special case of first-order functional autoregressive processes because of their technical tractability and the ...
Aue, Alexander +2 more
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A comparative evaluation of nonlinear dynamics methods for time series prediction [PDF]
, 2009 A key problem in time series prediction using autoregressive models is to fix the model order, namely the number of past samples required to model the time series adequately. The estimation of the model order using cross-validation may be a long process.
Camastra, F., Filippone, M.
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Chaotic Time Series Prediction Using Immune Optimization Theory [PDF]
International Journal of Computational Intelligence Systems, 2010 To solve chaotic time series prediction problem, a novel Prediction approach for chaotic time series based on Immune Optimization Theory (PIOT) is proposed.
Yuanquan Shi +6 more
doaj +1 more source
Multivariate time series prediction based on ARCLSTM
Journal of Measurement Science and Instrumentation, 2021 Time series is a kind of data widely used in various fields such as electricity forecasting, exchange rate forecasting, and solar power generation forecasting, and therefore time series prediction is of great significance.
QIAO Gangzhu, SU Rong, ZHANG Hongfei
doaj
Online Learning for Time Series Prediction [PDF]
, 2013 In this paper we address the problem of predicting a time series using the ARMA (autoregressive moving average) model, under minimal assumptions on the noise terms. Using regret minimization techniques, we develop effective online learning algorithms for
Anava, Oren +3 more
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Pediatric Blood &Cancer, EarlyView.ABSTRACT Background An international Delphi panel of experts developed consensus statements to delineate the circumstances where the risks of dexamethasone as an antiemetic do and do not outweigh its benefits. Procedure Experts in supportive care of pediatric patients were invited to participate.
Negar Shavandi +20 more
wiley +1 more source
Hetero-Dimensional Multitask Neuroevolution for Chaotic Time Series Prediction
IEEE Access, 2020 Chaotic time series prediction has important research and application value, and neural network-based prediction methods have problems such as low accuracy and difficulty in determining the number of nodes in the hidden layer.
Daoqing Zhang, Mingyan Jiang
doaj +1 more source
Temperature prediction using fuzzy time series [PDF]
IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 2000 A drawback of traditional forecasting methods is that they can not deal with forecasting problems in which the historical data are represented by linguistic values. Using fuzzy time series to deal with forecasting problems can overcome this drawback. In this paper, we propose a new fuzzy time series model called the two-factors time-variant fuzzy time ...
Chen, S. M., Hwang, J. R.
openaire +3 more sources