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Purpose: Bitcoin has indeed been universally acknowledged as an investment asset in recent decades, after the boom-and-bust of cryptocurrency values. Because of its extreme volatility, it requires accurate forecasts to build economic decisions. Although prior research has utilized machine learning to improve Bitcoin price prediction accuracy, few have looked into the plausibility of using multiple modeling approaches on datasets containing varying data types and volumetric attributes. Thus, this paper aims to propose a bitcoin price prediction model. Design/methodology/approach: In this research work, a bitcoin price prediction model is introduced by following three major phases: Data collection, feature extraction and price prediction. Initially, the collected Bitcoin time-series data will be preprocessed and the original features will be extracted. To make this work good-fit with a high level of accuracy, we have been extracting the second order technical indicator based features like average true range (ATR), modified-exponential moving average (M-EMA), relative strength index and rate of change and proposed decomposed inter-day difference. Subsequently, these extracted features along with the original features will be subjected to prediction phase, where the prediction of bitcoin price value is attained precisely from the constructed two-level ensemble classifier. The two-level ensemble classifier will be the amalgamation of two fabulous classifiers: optimized convolutional neural network (CNN) and bidirectional long/short-term memory (BiLSTM). To cope up with the volatility characteristics of bitcoin prices, it is planned to fine-tune the weight parameter of CNN by a new hybrid optimization model. The proposed hybrid optimization model referred as black widow updated rain optimization (BWURO) model will be conceptual blended of rain optimization algorithm and black widow optimization algorithm. Findings: The proposed work is compared over the existing models in terms of convergence, MAE, MAPE, MARE, MSE, MSPE, MRSE, Root Mean Square Error (RMSE), RMSPE and RMSRE, respectively. These evaluations have been conducted for both algorithmic performance as well as classifier performance. At LP = 50, the MAE of the proposed work is 0.023372, which is 59.8%, 72.2%, 62.14% and 64.08% better than BWURO + Bi-LSTM, CNN + BWURO, NN + BWURO and SVM + BWURO, respectively. Originality/value: In this research work, a new modified EMA feature is extracted, which makes the bitcoin price prediction more efficient. In this research work, a two-level ensemble classifier is constructed in the price prediction phase by blending the Bi-LSTM and optimized CNN, respectively. To deal with the volatility of bitcoin values, a novel hybrid optimization model is used to fine-tune the weight parameter of CNN. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of the paper is to better measure the risks and volatility of the Bitcoin market by using the proposed novel risk measurement model. Design/methodology/approach: The joint regression analysis of value at risk (VaR) and expected shortfall (ES) can effectively overcome the non-elicitability problem of ES to better measure the risks and volatility of financial markets. And because of the incomparable advantages of the long- and short-term memory (LSTM) model in processing non-linear time series, the paper embeds LSTM into the joint regression combined forecasting framework of VaR and ES, constructs a joint regression combined forecasting model based on LSTM for jointly measuring VaR and ES, i.e. the LSTM-joint-combined (LSTM-J-C) model, and uses it to investigate the risks of the Bitcoin market. Findings: Empirical results show that the proposed LSTM-J-C model can improve forecasting performance of VaR and ES in the Bitcoin market more effectively compared with the historical simulation, the GARCH model and the joint regression combined forecasting model. Social implications: The proposed LSTM-J-C model can provide theoretical support and practical guidance to cryptocurrency market investors, policy makers and regulatory agencies for measuring and controlling cryptocurrency market risks. Originality/value: A novel risk measurement model, namely LSTM-J-C model, is proposed to jointly estimate VaR and ES of Bitcoin. On the other hand, the proposed LSTM-J-C model provides risk managers more accurate forecasts of volatility in the Bitcoin market. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to develop a system dynamics approach based on Susceptible, Exposed, Infected, Recovered (SEIR) model to investigate the coronavirus pandemic and the impact of therapeutic and preventive interventions on epidemic disaster. Design/methodology/approach: To model the behavior of COVID-19 disease, a system dynamics model is developed in this paper based on SEIR model. In the proposed model, the impact of people's behavior, contact reduction, isolation of the sick people as well as public quarantine on the spread of diseases is analyzed. In this model, data collected by the Iran Ministry of Health have been used for modeling and verification of the results. Findings: The results show that besides the intervening policies, early application of them is also of utmost priority and makes a significant difference in the result of the system. Also, if the number of patients with extreme conditions passes available hospital intensive care capacity, the death rate increases dramatically. Intervening policies play an important role in reducing the rate of infection, death and consequently control of pandemic. Also, results show that if proposed policies do not work before the violation of the hospital capacity, the best policy is to increase the hospital's capacity by adding appropriate equipment. Research limitations/implications: The authors also had some limitations in the study including the lack of access to precise data regarding the epidemic of coronavirus, as well as accurate statistics of death rate and cases in the onset of the virus due to the lack of diagnostic kits in Iran. These parameters are still part of the problem and can negatively influence the effectiveness of intervening policies introduced in this paper. Originality/value: The contribution of this paper includes the development of SEIR model by adding more policymaking details and considering the constraint of the hospital and public health capacity in the rate of coronavirus infection and death within a system dynamics modeling framework. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to make a prediction of the R&D output of China from the perspective of R&D institutions and put forward a set of policy recommendations for further development of the science and technology in China. Design/methodology/approach: In this paper, an improved discrete grey multivariable model is proposed, which takes both the interaction effects and the accumulative effects into account. As the current research on China's R&D activities is generally based on the perspective of universities or industrial enterprises above designated size while few studies put their focus on R&D institutions, this paper applies the proposed model to carry out an empirical analysis based on the data of China's R&D institutions from 2009 to 2019. The prediction results from the new model are then compared with three existing approaches and the comparison results indicate that the proposed model generally outperforms existing methods. A further prediction of the R&D output in China's R&D institutions is conducted into a future horizon from 2020 to 2023 by using the model. Findings: The results indicate that China's R&D institutions have a good development trend and broad prospects, which is closely related to China's long-term investment in science and technology. Additionally, the R&D inputs of China possess obvious interaction effects and accumulative effects. Originality/value: The paper considers the interaction effects and the accumulative effects of R&D inputs simultaneously and proposes an improved discrete grey multivariable model, which fills the gap in previous studies. [ABSTRACT FROM AUTHOR]

Purpose: Customer churn prediction is one of the most well-known approaches to manage and improve customer retention. Machine learning techniques, especially classification algorithms, are very popular tools to predict the churners. In this paper, three ensemble classifiers are proposed based on bagging and boosting for customer churn prediction. Design/methodology/approach: In this paper, three ensemble classifiers are proposed based on bagging and boosting for customer churn prediction. The first classifier, which is called boosted bagging, uses boosting for each bagging sample. In this approach, before concluding the final results in a bagging algorithm, the authors try to improve the prediction by applying a boosting algorithm for each bootstrap sample. The second proposed ensemble classifier, which is called bagged bagging, combines bagging with itself. In the other words, the authors apply bagging for each sample of bagging algorithm. Finally, the third approach uses bagging of neural network with learning based on a genetic algorithm. Findings: To examine the performance of all proposed ensemble classifiers, they are applied to two datasets. Numerical simulations illustrate that the proposed hybrid approaches outperform the simple bagging and boosting algorithms as well as base classifiers. Especially, bagged bagging provides high accuracy and precision results. Originality/value: In this paper, three novel ensemble classifiers are proposed based on bagging and boosting for customer churn prediction. Not only the proposed approaches can be applied for customer churn prediction but also can be used for any other binary classification algorithms. [ABSTRACT FROM AUTHOR]

Purpose: Accurate foreign tourist arrivals forecasting can help public and private sectors to formulate scientific tourism planning and improve the allocation efficiency of tourism resources. This paper aims to address the problem of low prediction accuracy of Chinese inbound tourism demand caused by the lack of valid historical data. Design/methodology/approach: A novel hybrid Chinese inbound tourism demand forecasting model combining fractional non-homogenous discrete grey model and firefly algorithm is constructed. In the proposed model, all adjustable parameters of the fractional non-homogenous discrete grey model are optimized simultaneously by the firefly algorithm. Findings: The data sets of annual foreign tourist arrivals to China are used to verify the validity of the proposed model. Experimental results show that the proposed method is effective and can be used as a useful predictor for the prediction of Chinese inbound tourism demand. Originality/value: The method proposed in this paper is effective and can be used as a feasible approach for forecasting the development trend of Chinese inbound tourism. [ABSTRACT FROM AUTHOR]

Purpose The purpose of this paper is to investigate the effectiveness of GM(1,1) model on linear growth sequences (LGS) by random experiments and global primary energy consumption is predicted as by the GM(1,1) and the autoregressive integrated moving average (ARIMA) model, which is used as a reference.Design/methodology/approach LGS generated randomly are used for GM(1,1) modeling. The results of the massive repeated random experiments are analyzed to test the effectiveness of the GM(1,1) model and global primary energy consumption is predicted using the GM(1,1) model and the ARIMA model.Findings The use of the GM(1,1) model is effective when used for a LGS and the model is proven to be reliable by the experiments. Global primary energy consumption is predicted with the GM(1,1) model and the ARIMA model as a case study, and the results show that GM(1,1) is quite good. Global primary energy consumption will increase by 1.03 percent in 2016.Originality/value The contribution of this paper includes the following: first, the applicability of the GM (1,1) model is further discussed with random experiments and it is feasible for a LGS; second, random experiments provide good proof that four data are enough for GM(1,1) modeling, and GM(1,1) model is reliable; third, prediction by using GM(1,1) model with small data is even better than time-series analysis in the case study. [ABSTRACT FROM AUTHOR]

Purpose – The purpose of this paper is to improve the accuracy of short time load forecasting to ensure the economical and safe operation of power systems. The traditional neural network applied in time series like load forecasting, easily plunges into local optimum and has a complicated learning process, leading to relatively slow calculating speed. On the basis of existing literature, the authors carried out studies in an effort to optimize a new recurrent neural network by wavelet analysis to solve the previous problems. Design/methodology/approach – The main technique the authors applied is referred to as echo state network (ESN). Detailed information has been acquired by the authors using wavelet analysis. After obtaining more information from original time series, different reservoirs can be built for each subsequence. The proposed method is tested by using hourly electricity load data from a southern city in China. In addition, some traditional methods are also applied for the same task, as contrast. Findings – The experiment has led the authors to believe that the optimized model is encouraging and performs better. Compared with standard ESN, BP network and SVM, the experimental results indicate that WS-ESN improves the prediction accuracy and has less computing consumption. Originality/value – The paper develops a new method for short time load forecasting. Wavelet decomposition is employed to pre-process the original load data. The approximate part associated with low frequencies and several detailed parts associated with high frequencies components give expression to different information from original data. According to this, suitable ESN is chosen for each sub-sequence, respectively. Therefore, the model combining the advantages of both ESN and wavelet analysis improves the result for short time load forecasting, and can be applied to other time series problem. [ABSTRACT FROM AUTHOR]

Purpose – The purpose of this paper is to construct a novel GM(0, N) model based on grey number sequences and to solve the problem of cost forecasting of commercial aircraft which puzzled managers, especially the factors of cost with uncertain information. Design/methodology/approach – Based on the definition of traditional GM(0, N) model, the paper considers the limited information and knowledge, and the algorithm of grey numbers with greyness and kernel was designed. A novel GM(0, N) model based on grey number sequences, named the IN-GM(0, N) model, is proposed according to the definition of the grey numbers algorithm. The steps of the proposed model are then given. Finally, a case of domestic commercial aircrafts is developed as an example, based on information gathering and model calculating. Findings – The results of this research indicate that the IN-GM(0,N) model is effective in cost calculating, providing reliable technical support for cost estimation of large-scale complex equipment including commercial aircraft. Practical implications – Cost forecasting of commercial aircraft can be solved effectively and the model can also be utilized to predicate other products. Originality/value – The paper succeeds in constructing a novel grey forecasting model. This work contributes significantly to improving grey forecasting theory and to undoubtedly propose more novel grey forecasting models. [ABSTRACT FROM AUTHOR]

Purpose – The purpose of this paper is to simplify the computation of parameter estimation in the grey linear regression model and solve the problem that the development coefficient could not be computed in some sequence data, such as short-term traffic flow. Design/methodology/approach – Starting from the limitation that can be identified in the equation and analyzing the range using the method to estimate parameters, this paper researches the modelling mechanism and the other forms which are equivalent with the original form. At the same time, this paper gives an estimation method and gets the relationship in various forms and the relationship between the model and GM(1,1) model. Findings – For the grey linear regression model, there exists a new method of parameter identification and three other forms as follows: the original form, the Whitenization equation and the connotation form. Practical implications – The method of parameter identification exposed in the paper expanded the scope of the application of the grey linear regression model, and it can be used to model and forecast the urban road short-time traffic flow. Originality/value – This paper has solved some complicated problems such as the parameter estimation computation in the grey linear regression model. In addition, three kinds of representation forms of the model and its relationship between the model and GM(1,1) have also been presented. Finally, its application of the model in a short-term traffic flow prediction has shown its superiority. [ABSTRACT FROM AUTHOR]

Purpose – The purpose of this paper is to propose a second relational grade based on the general grey relational grade and analyze several of its properties. Design/methodology/approach – Grey system theory. The paper proposes and studies second grey relational grade, establishes second grey relational formula, and studies several characteristics of second grey relational formula. Findings – Proposing a second relational grade proved it could solve the problem of the parallelism partly and weaken relativity of space position. Research limitations/implications – Until now, the problem of the consistency could not be solved, nor could the problem of the effect which keeps the sequence the same. Practical implications – The precision of the grey forecasting model could be strengthened if used in the forecasting model. Originality/value – The general relational grade only thinks over the relation between two sequences but does not involve the relation in one sequence. The second relational grade considers these two, so if the forecasting model is established with it, the model should be more exact. [ABSTRACT FROM AUTHOR]

Purpose – The purpose of this paper is to improve forecasting accuracy for short-term load series. Design/methodology/approach – A forecasting method based on chaotic time series and optimal diagonal recurrent neural networks (DRNN) is presented. The input of the DRNN is determined by the embedding dimension of the reconstructed phase space, and adaptive dynamic back propagation (DBP) algorithm is used to train the network. The connection weights of the DRNN are optimized via modified genetic algorithms, and the best results of optimization are regarded as initial weights for the network. The new method is applied to predict the actual short-term load according to its chaotic characteristics, and the forecasting results also validate the feasibility. Findings – For the chaos time series, the hybrid neural genetic method based on phase space reconstruction can carry out the short-term prediction with the higher accuracy. Research limitations/implications – The proposed method is not suited to medium and long-term load forecasting. Practical implications – The accuracy of the load forecasting is important to the economic and secure operation of power systems; also, the neural genetic method can improve forecasting accuracy. Originality/value – This paper will help overcome the defects of traditional neural network and make short-term load forecasting more accurate and fast. [ABSTRACT FROM AUTHOR]

Purpose – Accurate forecasting of intermittent demand is very important since parts with intermittent demand characteristics are very common. The purpose of this paper is to bring an easier way of handling the hard work of intermittent demand forecasting by using commonly used Excel spreadsheet and also performing parameter optimization. Design/methodology/approach – Smoothing parameters of the forecasting methods are optimized dynamically by Excel Solver in order to achieve the best performance. Application is done on real data of Turkish Airlines’ spare parts comprising 262 weekly periods from January 2009 to December 2013. The data set are composed of 500 stock-keeping units, so there are 131,000 data points in total. Findings – From the results of implementation, it is shown that using the optimum parameter values yields better performance for each of the methods. Research limitations/implications – Although it is an intensive study, this research has some limitations. Since only real data are considered, this research is limited to the aviation industry. Practical implications – This study guides market players by explaining the features of intermittent demand. With the help of the study, decision makers dealing with intermittent demand are capable of applying specialized intermittent demand forecasting methods. Originality/value – The study brings simplicity to intermittent demand forecasting work by using commonly used spreadsheet software. The study is valuable for giving insights to market players dealing with items having intermittent demand characteristics, and it is one of the first study which is optimizing the smoothing parameters of the forecasting methods by using spreadsheet in the area of intermittent demand forecasting. [ABSTRACT FROM AUTHOR]