Results 51 to 60 of about 1,758,719 (322)
Applied Sciences, 2022 The Key Laboratory of Integrated Microsystems (IMS) of Peking University Shenzhen Graduate School has deployed a self-developed acoustic and electromagnetics to artificial intelligence (AETA) system on a large scale and at a high density in China to ...
Chenyang Wang +4 more
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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
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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
core
Network topology drives population temporal variability in experimental habitat networks
Population Ecology, EarlyView.Habitat patches connected by dispersal pathways form habitat networks. We explored how network topology affects population outcomes in laboratory experiments using a model species (Daphnia carinata). Central habitat nodes in complex lattice networks exhibited lower temporal variability in population sizes, suggesting they support more stable ...
Yiwen Xu +3 more
wiley +1 more source
Time Series Prediction Method of Bank Cash Flow and Simulation Comparison
Algorithms, 2014 In order to improve the accuracy of all kinds of information in the cash business and enhance the linkage between cash inventory forecasting and cash management information in the commercial bank, the first moving average prediction method, the second ...
Wen-Hua Cui +2 more
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On Prediction in Stationary Time Series [PDF]
The Annals of Mathematical Statistics, 1948 In time series analysis there are two lines of approach, here called the functional and the stochastic. In the former case, the given time series is interpreted as a mathematical function, in the latter case as a random specimen out of a universe of mathematical functions. The close relation between the two approaches is in section 2 shown to amount to
openaire +2 more sources
occumb: An R package for site occupancy modeling of eDNA metabarcoding data
Population Ecology, EarlyView.This study introduces a new R package, occumb, for the convenient application of site occupancy modeling using environmental DNA (eDNA) metabarcoding data. We outline a data analysis workflow, including data setup, model fitting, model assessment, and comparison of potential study settings based on model predictions, all of which can be performed using
Keiichi Fukaya, Yuta Hasebe
wiley +1 more source
Time Series Prediction Based on Complex-Valued S-System Model
Complexity, 2020 Symbolic regression has been utilized to infer mathematical formulas in order to solve the complex prediction and classification problems. In this paper, complex-valued S-system model (CVSS) is proposed to predict real-valued time series data.
Bin Yang, Wenzheng Bao, Yuehui Chen
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Employment the black box models to forecast the central bank’s foreign currency sales [PDF]
المجلة العراقية للعلوم الاحصائية, 2020 The research is aims to forecasting multi-variable time series using black box models that link the input series with the output series with a mathematical model as it includes two types of models, which are equation error models and output error models,
Afrah Hassan, Najlaa Ibrahim
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Bayesian long-run prediction in time series models [PDF]
, 1992 This paper considers Bayesian long-run prediction in time series models. We allow time series to exhibit stationary or non-stationary behavior and show how differences between prior structures which have little effect on posterior inferences can have a ...
Koop, Gary +2 more
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