scholarly journals Predictive model of effective sustainable operation for sustainable development of enterprises

2020 ◽  
Vol 214 ◽  
pp. 02044
Author(s):  
Haitong Wang ◽  
Ziyi Cheng ◽  
Hongyan Li
Keyword(s):  
Capital Market ◽  
Information Disclosure ◽  
Prediction Accuracy ◽  
Forecasting Model ◽  
Business Operation ◽  
Forecasting Models ◽  
Sustainable Operation ◽  
Business Forecasting ◽  
Chinese Scholars ◽  
Cart Model

Business forecasting has a very important impact on the future development of listed companies. Especially in the current era of information, corporate financial information disclosure is more comprehensive, so a reasonable business forecasting model is particularly important in the market. For the study of business operation forecasting models, Chinese scholars have achieved relevant results. This article is mainly based on the existing models for innovation and development. By establishing two models, SR + CART and ANN + CART, and testing their prediction accuracy, it provides a more diverse and reasonable tool for business forecasting, which is beneficial to the efficient development of capital market. The results show that the ANN + CART model has higher prediction accuracy, and the overall prediction accuracy is 92%.

IPO UNDERPRICING AND INFORMATION QUALITY OF PROSPECTUSES

2020 ◽  
Vol 65 (06) ◽  
pp. 1559-1577
Author(s):  
KUO ZHOU ◽  
BAICHENG ZHOU ◽  
HUAXIAO LIU
Keyword(s):  
Capital Market ◽  
Information Disclosure ◽  
Information Quality ◽  
Public Information ◽  
Relevant Information ◽  
Ipo Underpricing ◽  
Quality Of Information ◽  
Sustainable Operation ◽  
The Relationship

Effective information disclosure is the cornerstone of sustainable operation of the capital market. In the IPO market, whether public information in the prospectus can be fully captured by investors largely depends on the quality of valuation-relevant information. Based on Chinese prospectuses, we create five unique indicators to measure the information quality and examine the relationship between information quality and IPO underpricing. We find that high quality of information disclosure results in less underpricing because they relieve serious information asymmetry between issuing companies and investors. We provide a new method to supervise and improve the quality of non-financial information disclosure.

Wind Speed Forecasting in Wind Farm

2014 ◽  
Vol 672-674 ◽  
pp. 306-309
Author(s):  
Hong Peng Liu ◽  
Xiao Di Zhang ◽  
Hong Sheng Li ◽  
Qing Wang
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Prediction Accuracy ◽  
Wind Farm ◽  
Rbf Neural Network ◽  
Forecasting Model ◽  
Training Time ◽  
Wind Speed Forecasting ◽  
Forecasting Models ◽  
Artificial Neural Network Method

Artificial neural network method was used to forecast the wind speed, and two wind speed forecasting models were built based on BP and RBF neural network methods. 24 hours continuous wind speed forecast was conducted for a single wind turbine in wind farm. The results show that the models built are reasonable and have high prediction accuracy. By comparing the two kinds of wind speed forecasting models, BP neural network forecasting model has higher prediction accuracy than RBF neural network forecasting model in wind speed, but it demands much more training time.

An Improved Forecasting Model of Fuzzy Time Series

2014 ◽  
Vol 678 ◽  
pp. 64-69 ◽  
Author(s):  
Hong Xu Wang ◽  
Jian Chun Guo ◽  
Hao Feng ◽  
Hai Long Jin
Keyword(s):  
Time Series ◽  
Prediction Accuracy ◽  
Time Series Forecasting ◽  
Fuzzy Time Series ◽  
Forecasting Model ◽  
Simulation Data ◽  
Historical Simulation ◽  
Forecasting Models ◽  
Forecasting Theory

Since Song and Chissom proposed fuzzy time series forecasting theory, already exceed in the 20 years. Scholars have proposed many fuzzy time series forecasting models, the prediction accuracy of historical simulation data continues to improve. Unfortunately has not hitherto given for fuzzy time series forecasting model about the data of unknown years. This paper presents an improved forecasting model of fuzzy time series. It may predict the historical simulation data, but also may predict the unknown year data.

scholarly journals A Neutrosophic Forecasting Model for Time Series Based on First-Order State and Information Entropy of High-Order Fluctuation

Entropy ◽  
2019 ◽  
Vol 21 (5) ◽  
pp. 455 ◽  
Author(s):  
Hongjun Guan ◽  
Zongli Dai ◽  
Shuang Guan ◽  
Aiwu Zhao
Keyword(s):  
Time Series ◽  
Information Entropy ◽  
Stock Exchange ◽  
Time Series Forecasting ◽  
High Order ◽  
Stock Index ◽  
Neutrosophic Set ◽  
Forecasting Model ◽  
Forecasting Models ◽  
Proposed Model

In time series forecasting, information presentation directly affects prediction efficiency. Most existing time series forecasting models follow logical rules according to the relationships between neighboring states, without considering the inconsistency of fluctuations for a related period. In this paper, we propose a new perspective to study the problem of prediction, in which inconsistency is quantified and regarded as a key characteristic of prediction rules. First, a time series is converted to a fluctuation time series by comparing each of the current data with corresponding previous data. Then, the upward trend of each of fluctuation data is mapped to the truth-membership of a neutrosophic set, while a falsity-membership is used for the downward trend. Information entropy of high-order fluctuation time series is introduced to describe the inconsistency of historical fluctuations and is mapped to the indeterminacy-membership of the neutrosophic set. Finally, an existing similarity measurement method for the neutrosophic set is introduced to find similar states during the forecasting stage. Then, a weighted arithmetic averaging (WAA) aggregation operator is introduced to obtain the forecasting result according to the corresponding similarity. Compared to existing forecasting models, the neutrosophic forecasting model based on information entropy (NFM-IE) can represent both fluctuation trend and fluctuation consistency information. In order to test its performance, we used the proposed model to forecast some realistic time series, such as the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX), the Shanghai Stock Exchange Composite Index (SHSECI), and the Hang Seng Index (HSI). The experimental results show that the proposed model can stably predict for different datasets. Simultaneously, comparing the prediction error to other approaches proves that the model has outstanding prediction accuracy and universality.

Study of a discrete grey forecasting model based on the quality cost characteristic curve

2017 ◽  
Vol 7 (3) ◽  
pp. 376-384 ◽  
Author(s):  
Wenjie Dong ◽  
Sifeng Liu ◽  
Zhigeng Fang ◽  
Xiaoyu Yang ◽  
Qian Hu ◽  
...  
Keyword(s):  
Prediction Accuracy ◽  
Characteristic Curve ◽  
Grey System Theory ◽  
Forecasting Model ◽  
Quality Level ◽  
Cost Models ◽  
Quality Cost ◽  
Content Type ◽  
Grey Forecasting ◽  
Grey Forecasting Model

Purpose The purpose of this paper is to clarify several commonly used quality cost models based on Juran’s characteristic curve. Through mathematical deduction, the lowest point of quality cost and the lowest level of quality level (often depicted by qualification rate) can be obtained. This paper also aims to introduce a new prediction model, namely discrete grey model (DGM), to forecast the changing trend of quality cost. Design/methodology/approach This paper comes to the conclusion by means of mathematical deduction. To make it more clear, the authors get the lowest quality level and the lowest quality cost by taking the derivative of the equation of quality cost and quality level. By introducing the weakening buffer operator, the authors can significantly improve the prediction accuracy of DGM. Findings This paper demonstrates that DGM can be used to forecast quality cost based on Juran’s cost characteristic curve, especially when the authors do not have much information or the sample capacity is rather small. When operated by practical weakening buffer operator, the randomness of time series can be obviously weakened and the prediction accuracy can be significantly improved. Practical implications This paper uses a real case from a literature to verify the validity of discrete grey forecasting model, getting the conclusion that there is a certain degree of feasibility and rationality of DGM to forecast the variation tendency of quality cost. Originality/value This paper perfects the theory of quality cost based on Juran’s characteristic curve and expands the scope of application of grey system theory.

scholarly journals Foundation Settlement Prediction Based on a Novel NGM Model

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Peng-Yu Chen ◽  
Hong-Ming Yu
Keyword(s):  
Prediction Accuracy ◽  
Forecasting Model ◽  
Foundation Settlement ◽  
Engineering Construction ◽  
Settlement Prediction ◽  
Exponential Law ◽  
Grey Forecasting ◽  
Index Sequence ◽  
Settlement Time ◽  
Time Sequences

Prediction of foundation or subgrade settlement is very important during engineering construction. According to the fact that there are lots of settlement-time sequences with a nonhomogeneous index trend, a novel grey forecasting model called NGM(1,1,k,c)model is proposed in this paper. With an optimized whitenization differential equation, the proposed NGM(1,1,k,c)model has the property of white exponential law coincidence and can predict a pure nonhomogeneous index sequence precisely. We used two case studies to verify the predictive effect of NGM(1,1,k,c)model for settlement prediction. The results show that this model can achieve excellent prediction accuracy; thus, the model is quite suitable for simulation and prediction of approximate nonhomogeneous index sequence and has excellent application value in settlement prediction.

scholarly journals Short-Term Load Forecasting in Smart Grids: An Intelligent Modular Approach

Energies ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 164 ◽  
Author(s):  
Ashfaq Ahmad ◽  
Nadeem Javaid ◽  
Abdul Mateen ◽  
Muhammad Awais ◽  
Zahoor Ali Khan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Execution Time ◽  
Smart Grids ◽  
Load Forecasting ◽  
Forecast Accuracy ◽  
Forecasting Model ◽  
Exogenous Variables ◽  
Forecasting Models ◽  
The Third

Daily operations and planning in a smart grid require a day-ahead load forecasting of its customers. The accuracy of day-ahead load-forecasting models has a significant impact on many decisions such as scheduling of fuel purchases, system security assessment, economic scheduling of generating capacity, and planning for energy transactions. However, day-ahead load forecasting is a challenging task due to its dependence on external factors such as meteorological and exogenous variables. Furthermore, the existing day-ahead load-forecasting models enhance forecast accuracy by paying the cost of increased execution time. Aiming at improving the forecast accuracy while not paying the increased executions time cost, a hybrid artificial neural network-based day-ahead load-forecasting model for smart grids is proposed in this paper. The proposed forecasting model comprises three modules: (i) a pre-processing module; (ii) a forecast module; and (iii) an optimization module. In the first module, correlated lagged load data along with influential meteorological and exogenous variables are fed as inputs to a feature selection technique which removes irrelevant and/or redundant samples from the inputs. In the second module, a sigmoid function (activation) and a multivariate auto regressive algorithm (training) in the artificial neural network are used. The third module uses a heuristics-based optimization technique to minimize the forecast error. In the third module, our modified version of an enhanced differential evolution algorithm is used. The proposed method is validated via simulations where it is tested on the datasets of DAYTOWN (Ohio, USA) and EKPC (Kentucky, USA). In comparison to two existing day-ahead load-forecasting models, results show improved performance of the proposed model in terms of accuracy, execution time, and scalability.

scholarly journals Time-dependent prediction degredation assessment of neural-networks-based TEC forecasting models

2003 ◽  
Vol 10 (6) ◽  
pp. 585-587 ◽  
Author(s):  
Th. D. Xenos ◽  
S. S. Kouris ◽  
A. Casimiro
Keyword(s):  
Neural Networks ◽  
Faraday Rotation ◽  
Prediction Error ◽  
Prediction Accuracy ◽  
Time Span ◽  
Time Dependent ◽  
Forecasting Models ◽  
Error Margin ◽  
The Difference ◽  
Tec Variability

Abstract. An estimation of the difference in TEC prediction accuracy achieved when the prediction varies from 1 h to 7 days in advance is described using classical neural networks. Hourly-daily Faraday-rotation derived TEC measurements from Florence are used. It is shown that the prediction accuracy for the examined dataset, though degrading when time span increases, is always high. In fact, when a relative prediction error margin of ± 10% is considered, the population percentage included therein is almost always well above the 55%. It is found that the results are highly dependent on season and the dataset wealth, whereas they highly depend on the foF2 - TEC variability difference and on hysteresis-like effect between these two ionospheric characteristics.

scholarly journals Operational Probabilistic Forecasting of Coastal Freak Waves by Using an Artificial Neural Network

2020 ◽  
Vol 8 (3) ◽  
pp. 165
Author(s):  
Dong-Jiing Doong ◽  
Shien-Tsung Chen ◽  
Ying-Chih Chen ◽  
Cheng-Han Tsai
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Forecasting Model ◽  
Probabilistic Forecasting ◽  
Freak Waves ◽  
Forecasting Models ◽  
Artificial Neural

Coastal freak waves (CFWs) are unpredictable large waves that occur suddenly in coastal areas and have been reported to cause casualties worldwide. CFW forecasting is difficult because the complex mechanisms that cause CFWs are not well understood. This study proposes a probabilistic CFW forecasting model that is an advance on the basis of a previously proposed deterministic CFW forecasting model. This study also develops a probabilistic forecasting scheme to make an artificial neural network model achieve the probabilistic CFW forecasting. Eight wave and meteorological variables that are physically related to CFW occurrence were used as the inputs for the artificial neural network model. Two forecasting models were developed for these inputs. Model I adopted buoy observations, whereas Model II used wave model simulation data. CFW accidents in the coastal areas of northeast Taiwan were used to calibrate and validate the model. The probabilistic CFW forecasting model can perform predictions every 6 h with lead times of 12 and 24 h. The validation results demonstrated that Model I outperformed Model II regarding accuracy and recall. In 2018, the developed CFW forecasting models were investigated in operational mode in the Operational Forecast System of the Taiwan Central Weather Bureau. Comparing the probabilistic forecasting results with swell information and actual CFW occurrences demonstrated the effectiveness of the proposed probabilistic CFW forecasting model.

Hybrid Forecasting Model of Reactive Load Based on FOA and Linear Exponential Smoothing

2013 ◽  
Vol 823 ◽  
pp. 500-504
Author(s):  
Yuan Sheng Huang ◽  
Te Li ◽  
Wei Pi
Keyword(s):  
Prediction Accuracy ◽  
Exponential Smoothing ◽  
Forecasting Model ◽  
Hybrid Forecasting ◽  
Parameter Values

During the prediction of Linear exponential smoothing model,the value of the coefficient has a little blindness,which is always valued by experience.This paper uses FOA model to optimize it in Linear exponential smoothing model,constructing the hybrid forecasting model this paper uses to predict the reactive load of a substation.Then,this paper uses the hybrid forecasting model to forecast reactive load.The results show that:compared with the traditional Linear exponential smoothing model,the hybrid forecasting model is effective not only on selecting parameter values ,but also improving the prediction accuracy of the reactive load to a big extent.

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