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Owing to highly volatile and chaotic external events, predicting future movements of cryptocurrencies is a challenging task. This paper advances a granular hybrid predictive modeling framework for predicting the future figures of Bitcoin (BTC), Litecoin (LTC), Ethereum (ETH), Stellar (XLM) and Tether (USDT) during normal and pandemic regimes.

Initially, the major temporal characteristics of the price series are examined. In the second stage, ensemble empirical mode decomposition (EEMD) and maximal overlap discrete wavelet transformation (MODWT) are used to decompose the original time series into two distinct sets of granular subseries. In the third stage, long- and short-term memory network (LSTM) and extreme gradient boosting (XGB) are applied to the decomposed subseries to estimate the initial forecasts. Lastly, sequential quadratic programming (SQP) is used to fetch the forecast by combining the initial forecasts.

Rigorous performance assessment and the outcome of the Diebold-Mariano’s pairwise statistical test demonstrate the efficacy of the suggested predictive framework. The framework yields commendable predictive performance during the COVID-19 pandemic timeline explicitly as well. Future trends of BTC and ETH are found to be relatively easier to predict, while USDT is relatively difficult to predict.

The robustness of the proposed framework can be leveraged for practical trading and managing investment in crypto market. Empirical properties of the temporal dynamics of chosen cryptocurrencies provide deeper insights.

According to the problem of crude oil price forecasting, the purpose of this paper is to propose a multi-step prediction method based on the empirical mode decomposition, long short-term memory network and GM (1,1) model.

First, the empirical mode decomposition method is used to decompose the crude oil price series into several components with different frequencies. Then, each subsequence is classified and synthesized based on the specific periodicity and other properties to obtain several components with different significant characteristics. Finally, all components are substituted into a suitable prediction model for fitting. LSTM models with different parameters are constructed for predicting specific components, which approximately and respectively represent short-term market disturbance and long-term influences. Rolling GM (1,1) model is constructed to simulate a series representing the development trend of oil price. Eventually, all results obtained from forecasting models are summarized to evaluate the performance of the model.

The model is respectively applied to simulate daily, weekly and monthly WTI crude oil price sequences. The results show that the model has high accuracy on the prediction, especially in terms of series representing long-term influences with lower frequency. GM (1,1) model has excellent performance on fitting the trend of crude oil price.

This paper combines GM (1,1) model with LSTM network to forecast WTI crude oil price series. According to the different characteristics of different sequences, suitable forecasting models are constructed to simulate the components.

The purpose of this paper is to propose an approach to achieve better accuracy in technology forecasting (TF) by providing the concepts of the service components and service composition based on the theory of the combining forecasts. Next, it adopts three quantitative analyses as service components to form service composition. This will support the need of more predictable TF, which raises the accuracy of the quantitative analysis and, at the same time, presents the service composition logic in a consistent manner in the form of customized TF.

This paper provides a systematic analysis of the technology forecasts for third‐generation (3G) telecommunication industry. This systematic approach mainly unifies the Bass model, logit model, and least squares analysis forecasting techniques, along with a reasonable assessment of the scope for the normal curve (±1 standard deviation), and attempts to find the maximum possibility frontier of the predictive value.

Through the integration and comparison of these three techniques, not only can the predicted values of the three forecasting methods be determined, but a preferred solution can also be derived through new methods, and in return, to investigate better accuracy and performances. Such an approach can also integrate the advantages of various methods to provide a prediction interval, as well as objective and realistic projections.

This envisaged concept of “service component and service composition” is an integration of backing up in TF instruments in selection and reselection, which in return, provide optimization of service composition and accuracy maximization, as well as better performance prediction. A well‐known limitation of this research is that sudden technology breakthroughs are often unforeseeable in the majority of main‐stream quantitative analyses.

Constructing a new effective approach as results of “service component and service composition” can be compared to the traditional research methods such as Delphi method or other mathematical algorithms. This method generally produces higher quality forecasts than those attained from a single source.

Information and communication technology (ICT) is increasingly being used in the home environment, making it a very important and interesting research topic for communication scientists. Future developments will influence the way and the extent to which ICT will be used in the home environment and therefore the way people look for information, communicate, make use of entertainment services and carry out transactions. However, it is still very difficult to make meaningful and accurate forecasts with regard to the possible future use and acceptance of ICT in people’s homes. Important reasons are, for example, that more and more market parties are involved in the development of innovative ICT products and services. This makes developments more complex and the outcomes more uncertain. Furthermore, consumers play an important role in the development of new ICT‐based information, communication, transaction and entertainment services. Since a precise prediction of the possible use of ICT in domestic environments in 2010 is hard to make, other methods of futures research must be used. Combining technological forecasting with scenario thinking is such a research method, whereon, technological forecasting shows the major trends in the specific technology domain, while scenarios cover the possible future worlds. By giving end‐users a central place in these scenarios, the diversity of the use and acceptance of innovative products and services is captured. Thus, the addition of scenarios to the technology trends gives insight into the possibilities (and impossibilities) of new ICT‐technologies and the way they may be used in the home environment.

This paper aims to test whether a structured application of persuasion principles might help improve advertising decisions. Evidence-based principles are currently used to improve decisions in other complex situations, such as those faced in engineering and medicine.

Scores were calculated from the ratings of 17 self-trained novices who rated 96 matched pairs of print advertisements for adherence to evidence-based persuasion principles. Predictions from traditional methods – 10,809 unaided judgments from novices and 2,764 judgments from people with some expertise in advertising and 288 copy-testing predictions – provided benchmarks.

A higher adherence-to-principles-score correctly predicted the more effective advertisement for 75 per cent of the pairs. Copy testing was correct for 59 per cent, and expert judgment was correct for 55 per cent. Guessing would provide 50 per cent accurate predictions. Combining judgmental predictions led to substantial improvements in accuracy.

Advertisements for high-involvement utilitarian products were tested on the assumption that persuasion principles would be more effective for such products. The measure of effectiveness that was available –day-after-recall – is a proxy for persuasion or behavioral measures.

Pretesting advertisements by assessing adherence to evidence-based persuasion principles in a structured way helps in deciding which advertisements would be best to run. That procedure also identifies how to make an advertisement more effective.

This is the first study in marketing, and in advertising specifically, to test the predictive validity of evidence-based principles. In addition, the study provides the first test of the predictive validity of the index method for a marketing problem.

This study explores whether a new machine learning method can more accurately predict the movement of stock prices.

This study presents a novel hybrid deep learning model, Residual-CNN-Seq2Seq (RCSNet), to predict the trend of stock price movement. RCSNet integrates the autoregressive integrated moving average (ARIMA) model, convolutional neural network (CNN) and the sequence-to-sequence (Seq2Seq) long–short-term memory (LSTM) model.

The hybrid model is able to forecast both linear and non-linear time-series component of stock dataset. CNN and Seq2Seq LSTMs can be effectively combined for dynamic modeling of short- and long-term-dependent patterns in non-linear time series forecast. Experimental results show that the proposed model outperforms baseline models on S&P 500 index stock dataset from January 2000 to August 2016.

This study develops the RCSNet hybrid model to tackle the challenge by combining both linear and non-linear models. New evidence has been obtained in predicting the movement of stock market prices.

The purpose of this paper is to formulate a coordinated inventory control model for growing items in a supply chain with farming, processing and retail operations. The farmer grows newborn items and then delivers them to a processor once the items mature. At the processing plant, the items are slaughtered, cut and packaged at a specified rate. The processor then delivers a certain number of equally sized shipments of processed items to a retailer who satisfies customer demand.

A cost minimisation inventory model describing the problem at hand is formulated with the number of shipments and the cycle time being the decision variables. A solution algorithm for solving the problem is presented and applied to a numerical example.

Opting for an integrated policy is favourable to all supply chain members. When the proposed model is compared to equivalent independent and equal-cycle time replenishment policies, the total cost savings amount to 3 and 14 per cent, respectively.

The model can serve as a guideline for procurement managers dealing with growing items to better their inventory management practices. Considerable cost savings in food production chains can be achieved through improved inventory control, and these savings can be used to cushion consumers against rising food prices.

Most previously published models on inventory management for growing items were formulated under the assumption that the items are grown and then sold to consumers instantaneously. In real food production systems, the items need to be transformed and packaged into a consumable form before customer demand is met. The model presented in this paper accounts for this and is therefore more realistic.

To predict the passenger volume reasonably and accurately, this paper fills the gap in the research of quarterly data forecast of railway passenger volume. The research results can also provide references for railway departments to plan railway operation lines reasonably and efficiently.

This paper intends to establish a seasonal cycle first order univariate grey model (GM(1,1) model) combing with a seasonal index. GM (1,1) is termed as the trend equation to fit the railway passenger volume in China from 2014 to 2018. The railway passenger volume in 2019 is used as the experimental data to verify the forecasting effect of the proposed model. The forecasting results of the seasonal cycle GM (1,1) model are compared with the traditional GM (1,1) model, seasonal grey model (SGM(1,1)), Seasonal Autoregressive Integrated Moving Average (SARIMA) model, moving average method and exponential smoothing method. Finally, the authors forecast the railway passenger volume from 2020 to 2022.

The quarterly data of national railway passenger volume have a clear tendency of cyclical fluctuations and show an annual growth trend. According to the comparison of the modeling results, the authors know that the seasonal cycle GM (1,1) model has the best prediction effect with the mean absolute percentage error of 1.32%. It is much better than the other models, reflecting the feasibility of the proposed model.

As the previous grey prediction model could not solve the series prediction problem with seasonal fluctuation, and there are few research studies on quarterly railway passenger volume forecasting, GM (1,1) model is taken as the trend equation and combined with the seasonal index to construct a combination forecasting model for accurate forecasting results in this study. Besides, considering the impact of the epidemic on passenger volume, the authors introduce a disturbance factor to deal with the forecasting results in 2020, making the modeling results more scientific, practical and referential.

A new method for forecasting wind turbine capacity of China is proposed through grey modelling technique.

First of all, the concepts of discrete grey model are introduced into the NGBM(1,1) model to reduce the discretization error from the differential equation to its discrete forms. Then incorporating the conformable fractional accumulation into the discrete NGBM(1,1) model is carried out to further improve the predictive performance. Finally, in order to effectively seek the emerging coefficients, namely, fractional order and nonlinear coefficient, the whale optimization algorithm (WOA) is employed to determine the emerging coefficients.

The empirical results show that the newly proposed model has a better prediction performance compared to benchmark models; the wind turbine capacity from 2019 to 2021 is expected to reach 275954.42 Megawatts in 2021. According to the forecasts, policy suggestions are provided for policy-makers.

By combing the fractional accumulation and the concepts of discrete grey model, a new method to improve the prediction performance of the NGBM(1,1) model is proposed. The newly proposed model is firstly applied to predict wind turbine capacity of China.

A time-varying grey Fourier model (TVGFM(1,1,N)) is proposed for the simulation of variable amplitude seasonal fluctuation time series, as the performance of traditional grey models can't catch the time-varying trend well.

The proposed model couples Fourier series and linear time-varying terms as the grey action, to describe the characteristics of variable amplitude and seasonality. The truncated Fourier order N is preselected from the alternative order set by Nyquist-Shannon sampling theorem and the principle of simplicity, then the optimal Fourier order is determined by hold-out method to improve the robustness of the proposed model. Initial value correction and the multiple transformation are also studied to improve the precision.

The new model has a broader applicability range as a result of the new grey action, attaining higher fitting and forecasting accuracy. The numerical experiment of a generated monthly time series indicates the proposed model can accurately fit the variable amplitude seasonal sequence, in which the mean absolute percentage error (MAPE) is only 0.01%, and the complex simulations based on Monte-Carlo method testify the validity of the proposed model. The results of monthly electricity consumption in China's primary industry, demonstrate the proposed model catches the time-varying trend and has good performances, where MAPE_F and MAPE_T are below 5%. Moreover, the proposed TVGFM(1,1,N) model is superior to the benchmark models, grey polynomial model (GMP(1,1,N)), grey Fourier model (GFM(1,1,N)), seasonal grey model (SGM(1,1)), seasonal ARIMA model seasonal autoregressive integrated moving average model (SARIMA) and support vector regression (SVR).

The parameter estimates and forecasting of the new proposed TVGFM are studied, and the good fitting and forecasting accuracy of time-varying amplitude seasonal fluctuation series are testified by numerical simulations and a case study.