scholarly journals Grey GM (1,1) model based on combinatorial buffer operator

2021 ◽  
Vol 292 ◽  
pp. 02062
Author(s):  
He Peng-xiang ◽  
Sun Sheng-xiang
Keyword(s):  
Prediction Error ◽  
Original Method ◽  
Data Series ◽  
Original Sequence ◽  
Economic Problems ◽  
Average Prediction Error ◽  
Emerging Industries ◽  
Model Based ◽  
Long Time ◽  
Different Order

Considering that some emerging industries have not developed for a long time, the amount of data available for forecasting future economic problems is relatively limited, complex and changeable. Based on the principle of combinatorial prediction, a combinatorial buffer operator based on different order buffer operators is proposed, and the correlation area between the generated sequence and the original sequence after the buffer operator is used as weighting criterion. The grey GM (1,1) prediction model based on the combined buffer operator was established, which effectively overcame the influence of abnormal data and restored the change rule of data series. The average prediction error of the data in literature [7] by using the combined buffer operator established in this paper was 0.98%. Compared with 6.89%, 11i.59% and 1.30% of the original method, the predction accuracy is significantly improved.

scholarly journals ВИКОРИСТАННЯ МЕТОДУ МОНТЕ-КАРЛО ДЛЯ МАРКОВСЬКИХ ЛАНЦЮГІВ ДЛЯ ПРОГНОЗУВАННЯ ПОШИРЕНОСТІ ІШЕМІЧНОЇ ХВОРОБИ СЕРЦЯ В УКРАЇНІ

2018 ◽  
Author(s):  
M. A. Ivanchuk ◽  
P. R. Ivanchuk
Keyword(s):  
Coronary Heart Disease ◽  
Markov Chain ◽  
Heart Disease ◽  
Prediction Error ◽  
Epidemiological Data ◽  
Average Prediction Error ◽  
Predictive Quality ◽  
Absorbing State ◽  
Discrete States ◽  
Long Time

In medical forecasting, there are often challenges in which it is necessary to assess the risk that is continuous for a long time, and important events can occur more than once. One of the ways of solving problems of this type is the use of Markov models.Markov models suggest that the patient is always in one of the finite numbers of discrete states of health, called the Markov states. All events are modeled as a transition from one state to another. In order for the Markov chain to end, it must contain at least one absorbing state from which the patient can't pass into other states. In medical models, such a condition is the death of the patient, as this is the only condition from which the patient can't escape. The purpose of this work is to build a Markov chain for predicting the prevalence of coronary heart disease in UkraineTo test the predictive quality, the model was built since 1996. The data obtained for the model for 1996-2012 were compared with known epidemiological data. The average prediction error is 3.2 %. We predict an increase in the incidence of coronary heart disease to 35 041.2 per 100 000 population in 2025.

scholarly journals Dimension Analysis-Based Model for Prediction of Shale Compressive Strength

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Xiangyu Fan ◽  
Fenglin Xu ◽  
Lin Chen ◽  
Qiao Chen ◽  
Zhiwei Liu ◽  
...  
Keyword(s):  
Compressive Strength ◽  
Regression Model ◽  
Nonlinear Regression ◽  
Prediction Error ◽  
Diameter Ratio ◽  
Nonlinear Regression Model ◽  
Average Prediction Error ◽  
Model Average ◽  
Dimension Analysis ◽  
Longmaxi Shale

The compressive strength of shale is a comprehensive index for evaluating the shale strength, which is linked to shale well borehole stability. Based on correlation analysis between factors (confining stress, height/diameter ratio, bedding angle, and porosity) and shale compressive strength (Longmaxi Shale in Sichuan Basin, China), we develop a dimension analysis-based model for prediction of shale compressive strength. A nonlinear-regression model is used for comparison. A multitraining method is used to achieve reliability of model prediction. The results show that, compared to a multi-nonlinear-regression model (average prediction error = 19.5%), the average prediction error of the dimension analysis-based model is 19.2%. More importantly, our dimension analysis-based model needs to determine only one parameter, whereas the multi-nonlinear-regression model needs to determine five. In addition, sensitivity analysis shows that height/diameter ratio has greater sensitivity to compressive strength than other factors.

World 2021: Pandemic and economic problems remain acute

2021 ◽  
pp. 5-20
Author(s):  
M. V. Ershov
Keyword(s):  
Economic System ◽  
Global Economy ◽  
Large Scale ◽  
Real Economy ◽  
Economic Problems ◽  
Support Measures ◽  
Long Time ◽  
Digital Money ◽  
Successful Business ◽  
The Stability

The global economy continues to grow, albeit mainly due to large-scale support measures from governments and regulators. Moreover, the latter are not sure about the prospects for such development, since the economies do not demonstrate the potential for independent growth. As a result, in order to stimulate it, regulators are forced to expand the range of their tools, mechanisms, approaches, otherwise the risks to the stability of the global financial and economic system increase. All this is happening against the background of negative rates, which have become virtually ubiquitous and persist for a long time. New growth records are being set in the stock markets, and their gap from the real economy is growing. A number of sectors are beginning to dominate, forming distortions and bubbles in the markets. In such conditions, the importance of digital money, ecosystems, etc. increases. Moreover, the faster and more efficiently regulators can integrate into these formats, the more successful business, the population, and the economy as a whole will be.

Capability of the Bayesian Forecasting Method to Predict Field Time Series

2018 ◽  
Vol 140 (12) ◽  
Author(s):  
Nicolò Gatta ◽  
Mauro Venturini ◽  
Lucrezia Manservigi ◽  
Giuseppe Fabio Ceschini ◽  
Giovanni Bechini
Keyword(s):  
Time Series ◽  
Prediction Error ◽  
Field Data ◽  
Single Step ◽  
Bayesian Forecasting ◽  
Virtual Sensors ◽  
Average Prediction Error ◽  
Prediction Scheme ◽  
Forecasting Method ◽  
Prediction Capability

This paper addresses the challenge of forecasting the future values of gas turbine measureable quantities. The final aim is the simulation of “virtual sensors” capable of producing statistically coherent measurements aimed at replacing anomalous observations discarded from the time series. Among the different available approaches, the Bayesian forecasting method (BFM) adopted in this paper uses the information held by a pool of observations as knowledge base to forecast the values at a future state. The BFM algorithm is applied in this paper to Siemens field data to assess its prediction capability, by considering two different approaches, i.e., single-step prediction (SSP) and multistep prediction (MSP). While SSP predicts the next observation by using true data as base of knowledge, MSP uses previously predicted data as base of knowledge to perform the prediction of future time steps. The results show that BFM single-step average prediction error can be very low, when filtered field data are analyzed. On the contrary, the average prediction error achieved in case of BFM multistep prediction is remarkably higher. To overcome this issue, the BFM single-step prediction scheme is also applied to clusters of time-wise averaged data. In this manner, an acceptable average prediction error can be achieved by considering clusters composed of 60 observations.

scholarly journals Prefrontal solution to the bias-variance tradeoff during reinforcement learning

2020 ◽  
Author(s):  
Dongjae Kim ◽  
Jaeseung Jeong ◽  
Sang Wan Lee
Keyword(s):  
Adaptive Control ◽  
Reinforcement Learning ◽  
Prediction Error ◽  
Brain Regions ◽  
Decision Task ◽  
Prediction Errors ◽  
Model Based ◽  
Model Free ◽  
Bias Variance ◽  
The Brain

AbstractThe goal of learning is to maximize future rewards by minimizing prediction errors. Evidence have shown that the brain achieves this by combining model-based and model-free learning. However, the prediction error minimization is challenged by a bias-variance tradeoff, which imposes constraints on each strategy’s performance. We provide new theoretical insight into how this tradeoff can be resolved through the adaptive control of model-based and model-free learning. The theory predicts the baseline correction for prediction error reduces the lower bound of the bias–variance error by factoring out irreducible noise. Using a Markov decision task with context changes, we showed behavioral evidence of adaptive control. Model-based behavioral analyses show that the prediction error baseline signals context changes to improve adaptability. Critically, the neural results support this view, demonstrating multiplexed representations of prediction error baseline within the ventrolateral and ventromedial prefrontal cortex, key brain regions known to guide model-based and model-free learning.One sentence summaryA theoretical, behavioral, computational, and neural account of how the brain resolves the bias-variance tradeoff during reinforcement learning is described.

scholarly journals Noise performance of microwave humidity sounders over their life time

2017 ◽  
Author(s):  
Imke Hans ◽  
Martin Burgdorf ◽  
Viju O. John ◽  
Jonathan Mittaz ◽  
Stefan A. Buehler
Keyword(s):  
Uncertainty Propagation ◽  
Life Time ◽  
Noise Spectrum ◽  
Data Series ◽  
Allan Deviation ◽  
Noise Performance ◽  
Climate Data ◽  
Time Data ◽  
Accessible Information ◽  
Long Time

Abstract. The microwave humidity sounders Special Sensor Microwave Water Vapour Profiler (SSMT-2), Advanced Microwave Sounding Unit-B (AMSU-B) and Microwave Humidity Sounder (MHS) to date have been providing data records for 25 years. So far, the data records lack uncertainty information essential for constructing consistent long time data series. In this study, we assess the quality of the recorded data with respect to the uncertainty caused by noise. We calculate the noise on the raw calibration counts from the deep space view (DSV) of the instrument and the Noise Equivalent Differential Temperature (NEΔT) as a measure for the radiometer sensitivity. For this purpose, we use the Allan Deviation that is not biased from an underlying varying mean of the data and that has been suggested only recently for application in atmospheric remote sensing. Moreover, we use the bias function related to the Allan Deviation to infer the underlying spectrum of the noise. As examples, we investigate the noise spectrum in flight for some instruments. For the assessment of the noise evolution in time, we provide a descriptive and graphical overview of the calculated NEΔT over the life span of each instrument and channel. This overview can serve as an easily accessible information for users interested in the noise performance of a specific instrument, channel and time. Within the time evolution of the noise, we identify periods of instrumental degradation, which manifest themselves in an increasing NEΔT, and periods of erratic behaviour, which show sudden increases of NEΔT interrupting the overall smooth evolution of the noise. From this assessment and subsequent exclusion of the aforementioned periods, we present a chart showing available data records with NEΔT < K. Due to overlapping life spans of the instruments, these reduced data records still cover without gaps the time since 1994 and may therefore serve as first step for constructing long time series. Our method for count noise estimation, that has been used in this study, will be used in the data processing to provide input values for the uncertainty propagation in the generation of a new set of Fundamental Climate Data Records (FCDR) that are currently produced in the project Fidelity and Uncertainty in Climate data records from Earth Observation (FIDUCEO).

scholarly journals VIEWS OF VOJVODINIAN LEADERSHIP ON RE-OPENING THE ISSUE OF THE RELATIONSHIP BETWEEN SR SERBIA AND SAP VOJVODINA IN 1981

1970 ◽  
pp. 258-273 ◽  
Author(s):  
SLOBODAN BJELICA
Keyword(s):  
Federal Republic ◽  
Economic Crisis ◽  
Working Group ◽  
Socialist Republic ◽  
Economic Problems ◽  
Long Time ◽  
The Republic ◽  
Autonomous Province ◽  
The Relationship ◽  
Comprehensive Studies

In the early 1980s, after the death of the long-time President Josip Broz Tito, the Socialist Federal Republic of Yugoslavia slowly began to fall into a deep political and economic crisis. One of the most important aspects of this crisis was the crisis between the republic and the province, whose relationship was based on the 1974 Constitution. In terms of relations of the Socialist Republic of Serbia and the Socialist Autonomous Province of Vojvodina the degradation started 1981, when in the wake of the Albanian demonstrations (i.e. the counterrevolution in Kosovo), the republic leadership demanded a redefinition of the relations within Serbia, i.e. the change of the Constitution. Responding to the specific criticism from Belgrade, Vojvodinian leaders formed a working group which, in eight comprehensive studies, gave their view of the normative and economic problems of Serbia and Vojvodina.

Symmetry vs. Duality in Logic

2014 ◽  
Vol 8 (4) ◽  
pp. 83-97 ◽  
Author(s):  
Giulia Battilotti
Keyword(s):  
Sequent Calculus ◽  
Logical Consequence ◽  
Quantum States ◽  
The Other ◽  
Human Reasoning ◽  
Symmetric Mode ◽  
The Unconscious ◽  
Structural Rules ◽  
Model Based ◽  
Long Time

The author discusses the problem of symmetry, namely of the orientation of the logical consequence. The author shows that the problem is surprisingly entangled with the problem of “being infinite”. The author presents a model based on quantum states and shows that it features satisfy the requirements of the symmetric mode of Bi-logic, a logic introduced in the '70s by the psychoanalyst I. Matte Blanco to describe the logic of the unconscious. The author discusess symmetry, in the model, to include correlations, in order to obtain a possible approach to displacement. In this setting, the author finds a possible reading of the structural rules of sequent calculus, whose role in computation, on one side, and in the representation of human reasoning, on the other, has been debated for a long time.

Prediction for Sound Radiated Power from Submerged Double Cylindrical Shells Based on Measuring Vibration of Inner Shell

2013 ◽  
Vol 779-780 ◽  
pp. 602-606 ◽  
Author(s):  
Chao Zhang ◽  
De Jiang Shang ◽  
Qi Li
Keyword(s):  
Prediction Error ◽  
Cylindrical Shells ◽  
Prediction Method ◽  
Superposition Method ◽  
Power Curve ◽  
Mean Square ◽  
Average Prediction Error ◽  
Radiated Power ◽  
Modal Superposition Method ◽  
Good Agreement

A prediction method for the sound radiated power from submerged double cylindrical shells based on measuring vibration of inner shell is presented. The prediction model of submerged double cylindrical shells is established by using modal superposition method. Applied the ratio of the measuring value and theoretical value of the acceleration in one point or mean square velocity of inner shell, and combined with the theoretical value of the sound radiated power, the predicted value of the sound radiated power is derived. The corresponding experiment is carried out in lake. And then the measuring power curve is compared with the predicted power curve based on this method. The result shows that they have good agreement and the average prediction error is less than 2dB.

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