scholarly journals A Rolling Optimized Nonlinear Grey Bernoulli Model RONGBM(1,1) and application in predicting total COVID-19 cases

2020 ◽  
Author(s):  
Hoang Anh NGO ◽  
Thai Nam HOANG
Keyword(s):  
Time Series ◽  
Small Time ◽  
Simultaneous Optimization ◽  
Grey Model ◽  
Model Parameters ◽  
Initial Condition ◽  
Bernoulli Model ◽  
New Model ◽  
Background Value ◽  
Novel Model

The Nonlinear Grey Bernoulli Model NGBM(1, 1) is a recently developed grey model which has various applications in different fields, mainly due to its accuracy in handling small time-series datasets with nonlinear variations. In this paper, to fully improve the accuracy of this model, a novel model is proposed, namely Rolling Optimized Nonlinear Grey Bernoulli Model RONGBM(1, 1). This model combines the rolling mechanism with the simultaneous optimization of all model parameters (exponential, background value and initial condition). The accuracy of this new model has significantly been proven through forecasting Vietnam’s GDP from 2013 to 2018, before it is applied to predict the total COVID-19 infected cases globally by day.

scholarly journals A Novel Optimized Nonlinear Grey Bernoulli Model for Forecasting China’s GDP

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Wen-Ze Wu ◽  
Tao Zhang ◽  
Chengli Zheng
Keyword(s):  
Decision Making ◽  
Prediction Accuracy ◽  
Small Sample ◽  
Initial Condition ◽  
Bernoulli Model ◽  
New Model ◽  
First Order ◽  
Proposed Model ◽  
Generating Operator ◽  
Sample Time

The nonlinear grey Bernoulli model, abbreviated as NGBM(1,1), has been successfully applied to control, prediction, and decision-making fields, especially in the prediction of nonlinear small sample time series. However, there are still some problems in improving the prediction accuracy of NGBM(1,1). In this paper, we propose a novel optimized nonlinear grey Bernoulli model for forecasting Chinaʼs GDP. In the new model, the structure and parameters of NGBM(1,1) are optimized simultaneously. Especially, the latest item of first-order accumulative generating operator (1-AGO) sequence is taken as the initial condition, then background value is reconstructed by optimizing weights of neighbor values in 1-AGO sequence, which is based on minimizing the sum of absolute percentage errors, and finally, we establish the new model based on the rolling mechanism. Prediction accuracy of the proposed model is investigated through some simulations and a real example application, and the proposed model is applied to forecast the annual GDP in China from 2019 to 2023.

scholarly journals The Effect of Rain–No Rain Intermittency on the Estimation of the Universal Multifractals Model Parameters

2009 ◽  
Vol 10 (2) ◽  
pp. 493-506 ◽  
Author(s):  
L. de Montera ◽  
L. Barthès ◽  
C. Mallet ◽  
P. Golé
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Rain Rate ◽  
Scientific Literature ◽  
Model Parameters ◽  
New Model ◽  
Dual Beam ◽  
Event Basis

Abstract The multifractal properties of rain are investigated within the framework of universal multifractals. The database used in this study includes measurements performed over several months in different locations by means of a disdrometer, the dual-beam spectropluviometer (DBS). An assessment of the effect of the rain–no rain intermittency shows that the analysis of rain-rate time series may lead to a spurious break in the scaling and to erroneous parameters. The estimation of rain multifractal parameters is, therefore, performed on an event-by-event basis, and they are found to be significantly different from those proposed in scientific literature. In particular, the parameter H, which has often been estimated to be 0, is more likely to be 0.53, thus meaning that rain is a fractionally integrated flux (FIF). Finally, a new model is proposed that simulates high-resolution rain-rate time series based on these new parameters and on a simple threshold.

A grey hysteresis model of magnetorheological damper

2021 ◽  
pp. 1045389X2110571
Author(s):  
Xiong Deng ◽  
Xiaomin Dong ◽  
Wenfeng Li ◽  
Jun Xi
Keyword(s):  
Predictive Ability ◽  
Mr Damper ◽  
Inverse Model ◽  
Magnetorheological Damper ◽  
Grey Model ◽  
Small Samples ◽  
Model Parameters ◽  
Grey Theory ◽  
Hysteresis Model ◽  
Novel Model

Owing to the complex nonlinear hysteresis of magnetorheological (MR) damper, the modeling of an MR damper is an issue. This paper examines a novel MR damper hysteresis model based on the grey theory, which can fully mine the internal laws for the data with small samples and poor information. To validate the model, the experiment is conducted in the MTS platform, and then the experimental results are compiled to identify the model parameters. Considering the complexity of the grey model and its inverse model solution, the grey model is simplified in two ways based on the grey relational analysis method. Furthermore, the simplified grey model compares to other models to prove the superiority of the grey model. The analysis suggests the fitting results correspond to the measured results, and the mean relative error (MRE) of grey model is within 2.04%. After the grey model is simplified, its accuracy is slightly reduced, while its inverse model is easier to solve and makes a unique solution. Finally, compared with the polynomial and Bouc-Wen model, the novel model with fewer identification parameters has high accuracy and predictive ability. This novel model has fabulous potential in designing the control strategy of MR damper.

scholarly journals QUANTILE DOUBLE AUTOREGRESSION

2021 ◽  
pp. 1-47
Author(s):  
Qianqian Zhu ◽  
Guodong Li
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Simulation Studies ◽  
Finite Sample ◽  
Conditional Quantile ◽  
New Model ◽  
Conditional Quantiles ◽  
Financial Time ◽  
Portmanteau Tests ◽  
Strict Stationarity

Many financial time series have varying structures at different quantile levels, and also exhibit the phenomenon of conditional heteroskedasticity at the same time. However, there is presently no time series model that accommodates both of these features. This paper fills the gap by proposing a novel conditional heteroskedastic model called “quantile double autoregression”. The strict stationarity of the new model is derived, and self-weighted conditional quantile estimation is suggested. Two promising properties of the original double autoregressive model are shown to be preserved. Based on the quantile autocorrelation function and self-weighting concept, three portmanteau tests are constructed to check the adequacy of the fitted conditional quantiles. The finite sample performance of the proposed inferential tools is examined by simulation studies, and the need for use of the new model is further demonstrated by analyzing the S&P500 Index.

Selective Linear Segmentation for Detecting Relevant Parameter Changes*

2020 ◽  
Author(s):  
Arnaud Dufays ◽  
Elysee Aristide Houndetoungan ◽  
Alain Coën
Keyword(s):  
Time Series ◽  
Hedge Fund ◽  
Expectation Maximization Algorithm ◽  
Model Parameters ◽  
Relevant Parameter ◽  
Time Varying ◽  
Long Time ◽  
Model Selection Uncertainty ◽  
Penalized Likelihood Approach ◽  
Changes Over Time

Abstract Change-point (CP) processes are one flexible approach to model long time series. We propose a method to uncover which model parameters truly vary when a CP is detected. Given a set of breakpoints, we use a penalized likelihood approach to select the best set of parameters that changes over time and we prove that the penalty function leads to a consistent selection of the true model. Estimation is carried out via the deterministic annealing expectation-maximization algorithm. Our method accounts for model selection uncertainty and associates a probability to all the possible time-varying parameter specifications. Monte Carlo simulations highlight that the method works well for many time series models including heteroskedastic processes. For a sample of fourteen hedge fund (HF) strategies, using an asset-based style pricing model, we shed light on the promising ability of our method to detect the time-varying dynamics of risk exposures as well as to forecast HF returns.

A novel fractional discrete grey model with an adaptive structure and its application in electricity consumption prediction

Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yitong Liu ◽  
Yang Yang ◽  
Dingyu Xue ◽  
Feng Pan
Keyword(s):  
Fractional Order ◽  
Electricity Consumption ◽  
Grey Model ◽  
The Novel ◽  
Raw Data ◽  
Content Type ◽  
Power Load ◽  
Grey Models ◽  
Novel Model ◽  
Consumption Prediction

PurposeElectricity consumption prediction has been an important topic for its significant impact on electric policies. Due to various uncertain factors, the growth trends of electricity consumption in different cases are variable. However, the traditional grey model is based on a fixed structure which sometimes cannot match the trend of raw data. Consequently, the predictive accuracy is variable as cases change. To improve the model's adaptability and forecasting ability, a novel fractional discrete grey model with variable structure is proposed in this paper.Design/methodology/approachThe novel model can be regarded as a homogenous or non-homogenous exponent predicting model by changing the structure. And it selects the appropriate structure depending on the characteristics of raw data. The introduction of fractional accumulation enhances the predicting ability of the novel model. And the relative fractional order r is calculated by the numerical iterative algorithm which is simple but effective.FindingsTwo cases of power load and electricity consumption in Jiangsu and Fujian are applied to assess the predicting accuracy of the novel grey model. Four widely-used grey models, three classical statistical models and the multi-layer artificial neural network model are taken into comparison. The results demonstrate that the novel grey model performs well in all cases, and is superior to the comparative eight models.Originality/valueA fractional-order discrete grey model with an adaptable structure is proposed to solve the conflict between traditional grey models' fixed structures and variable development trends of raw data. In applications, the novel model has satisfied adaptability and predicting accuracy.

APPLING GREY FORECASTING METHOD TO FORECAST THE PORTFOLIO’S RATE OF RETURN IN STOCK MARKET OF IRAN

2012 ◽  
Vol 01 (07) ◽  
pp. 01-16
Author(s):  
Ali Mohammadi ◽  
Sara Zeinodin Zade
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Predictive Ability ◽  
Grey Model ◽  
Rate Of Return ◽  
Forecasting Accuracy ◽  
Grey Forecasting ◽  
Best Value ◽  
Forecasting Method ◽  
Time Series Method

Stock market is one of the most important investment market, which influenced by many factors, therefore it needs a robust and accurate forecasting. In this study ,grey model used as a forecasting method and examined if it is the most reliable forecasting method in comparison of time series method. The information of portfolio’s rate of return is gathered from 50 accepted companies in Tehran stock market, which were announced as the best companies last year. Mean Square of the errors (MSE) is computed by different value of α in grey model which could be varied between .1 to .9 ,to examined if α=.5 is the best value that our model could take .Then the predictive ability of the model is compared with different type of time series based forecasting methods Experimental results confirm forecasting accuracy of grey model. Tracking signal is computed for grey model to see whether grey model forecasting is in control or not. At the last portfolio’s rate of return is forecasted for next periods.

A Material Removal Model for Nonconstant-contact Flexible Grinding

2021 ◽  
Author(s):  
Huan Ye ◽  
Zhitong Chen ◽  
Zhuoqun Xie ◽  
Shangbin Li ◽  
Shuai Su
Keyword(s):  
Material Removal ◽  
Model Parameters ◽  
Grinding Process ◽  
Element Analysis ◽  
The Finite Element Analysis ◽  
Material Removal Model ◽  
Contact Situation ◽  
Equivalent Method ◽  
Removal Model ◽  
Novel Model

Abstract Contact calculation is of great importance in predicting the material removal (MR) of flexible grinding process (FGP). The contact is mostly considered approximately constant in the existing MR models, while the situations that contact varies a lot after FGP are ignored. Therefore, a novel model is proposed in this paper to take those situations into consideration. Firstly, the nonconstant-contact situation is introduced. Then an equivalent method is developed to convert the nonconstant-contact grinding process into the accumulation of several quasi-constant-contact grinding processes. Based on the equivalent method, a MR model is established, and the procedure to obtain the model parameters by the finite element analysis (FEA) is introduced. In the end, the equivalent method and the MR model are tested by a series experiments of different process parameters. Results show that the proposed MR model can predict the material removal effectively for the nonconstant-contact situations.

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