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In the current study, two grey prediction models, Even GM (1, 1) and Non-homogeneous discrete grey model (NDGM), and ARIMA models are deployed to forecast cocoa bean production of the six major cocoa-producing countries. Furthermore, relying on Relative Growth Rate (RGR) and Doubling Time (D_t), production growth is analyzed.

The secondary data were extracted from the United Nations Food and Agricultural Organization (FAO) database. Grey forecasting models are applied using the data covering 2008 to 2017 as their performance on the small sample size is well-recognized. The models' performance was estimated through MAPE, MAE and RMSE.

Results show the two grey models fell below 10% of MAPE confirming their high accuracy and forecasting performance against that of the ARIMA. Therefore, the suitability of grey models for the cocoa production forecast is established. Findings also revealed that cocoa production in Côte d'Ivoire, Cameroon, Ghana and Brazil is likely to experience a rise with a growth rate of 2.52, 2.49, 2.45 and 2.72% by 2030, respectively. However, Nigeria and Indonesia are likely to experience a decrease with a growth rate of 2.25 and 2.21%, respectively.

For a sustainable cocoa industry, stakeholders should investigate the decline in production despite the implementation of advanced agricultural mechanization in cocoa farming, which goes further to put food security at risk.

The study presents a pioneering attempt of using grey forecasting models to predict cocoa production.

Booms and bubbles are inevitable in the real estate industry. Loss of profits, bankruptcy and economic slowdown are indicators of the adverse effects of fluctuations in property prices. Models providing a reliable forecast of property prices are vital for mitigating the effects of these variations. Hence, this study aims to investigate the use of artificial intelligence (AI) for the prediction of property price index (PPI).

Information on the variables that influence property prices was collected from reliable sources in Hong Kong. The data were fitted to an autoregressive integrated moving average (ARIMA), artificial neural network (ANN) and support vector machine (SVM) models. Subsequently, the developed models were used to generate out-of-sample predictions of property prices.

Based on the prediction evaluation metrics, it was revealed that the ANN model outperformed the SVM and ARIMA models. It was also found that interest rate, unemployment rate and household size are the three most significant variables that could influence the prices of properties in the study area.

The findings of this study provide useful information to stakeholders for policy formation and strategies for real estate investments and sustained growth of the property market.

The application of the SVM model in the prediction of PPI in the study area is lacking. This study evaluates its performance in relation to ANN and ARIMA.

Technical analysis lies on the premiss that short‐term market price at any time is revealed by pattern of prior price movements. Tests empirically the pattern of the real estate prices by employing the ARIMA analysis. Results strongly show that there exist cyclical trends in the office and industrial property prices in Hong Kong. The forecasting method can provide an indication of short‐term market direction, a sense of whether or not the movement will be small or large, and advance warning well ahead of any turning points supplementary to investment strategy. The investor may wish to incorporate forecasts from an ARIMA model into his investment strategy, for timing purposes.

The purpose of this paper is to propose an integrated approach to modeling and measuring supply chain performance and stability using system dynamics (SD) and the autoregressive integrated moving average (ARIMA).

SD and ARIMA models were developed, respectively, for modeling and measuring supply chain performance and for further analyzing and projecting supply chain stability for long‐term management. A case study from a typical semiconductor equipment manufacturing company is used to illustrate and validate the proposed method.

Effectiveness and efficiency, with six corresponding indicators (product reliability, employee fulfillment, customer fulfillment, on‐time delivery, profit growth, and working efficiency), were found to be the most significant factors in the performance of the supply chain. The results of the combined model provide evidence that supply chain performance of the case company is up to standard (average OPIN=0.64) and is considered stable, but still far from outstanding. Continuous improvement, especially in supply chain efficiency, is suggested in order to maximize performance.

This integrated approach is innovative and creates a new way for other disciplines. This study provides a practical and easy‐to‐use model that enables senior and top management decision makers and operation managers involved in the supply chain to assess, forecast, and take anticipatory action so that the supply chain can experience improvement in a timesaving and effective manner and achieve excellence in performance.

Fluctuations in construction output has an adverse effect on the construction industry and the economy due to its strong linkage. Developing reliable and accurate predictive models is vital to implementing effective response strategies to mitigate the impact of such fluctuations. The purpose of this paper is to compare the accuracy of two univariate forecast models, i.e. Box-Jenkins (autoregressive integrated moving average (ARIMA)) and Neural Network Autoregressive (NNAR).

Four quarterly time-series data on the construction output of Hong Kong were collected (1983Q1-2014Q4). The collected data were divided into two parts. The first part was fitted to the model, while the other was used to evaluate the predictive accuracy of the developed models.

The NNAR model can provide reliable and accurate forecast of total, private and “others” construction output for the medium term. In addition, the NNAR model outperforms the ARIMA model, in terms of accuracy.

The applicability of the NNAR model to the construction industry of other countries could be further explored. The main limitation of artificial intelligence models is the lack of explanatory capability.

The NNAR model could be used as a tool for accurately predicting future patterns in construction output. This is vital for the sustained growth of the construction industry and the economy.

This is the first study to apply the NNAR model to construction output forecasting research.

The purpose of this study is to analyze the fluctuations in gold prices within the Saudi Arabian market and to develop a reliable forecasting model to aid market participants and policymakers in making informed decisions.

In this study, we employ a rigorous time series analysis methodology, including the ARIMA (Auto Regressive Integrated Moving Average) model, to analyze historical gold price data in the Saudi Arabian market. The approach involves identifying optimal model parameters and assessing forecast accuracy to provide actionable insights for market participants.

The study showcases that the autoregressive properties of past gold prices play a pivotal role in capturing the inherent serial correlation within the market, enabling the ARIMA model to effectively forecast future gold price movements with accuracy.

Our study primarily focuses on quantitative analysis, whereas few qualitative parameters are not included. Future studies may benefit from incorporating qualitative factors and expert opinions to enhance the robustness of gold price predictions and capture the full spectrum of market dynamics.

Participants and policymakers may find this study helpful in navigating the complicated Saudi Arabian gold market. By understanding financial stability and investment decisions more thoroughly, individuals and institutions may be able to manage their portfolios more effectively.

By combining historical insights with advanced ARIMA modeling techniques, this research provides valuable insight into gold price dynamics in the Saudi Arabian market.

Retail sales usually exhibit strong trend and seasonal patterns. Practitioners have typically used seasonal autoregressive integrated moving average (ARIMA) models to predict retail sales exhibiting these patterns. Due to economic instability, recent retail sales time-series data show a higher degree of variability and nonlinearity, which makes the ARIMA model less accurate. This chapter demonstrates the feasibility and potential of applying empirical mode decomposition (EMD) in forecasting aggregate retail sales. The hybrid forecasting method of integrating EMD and neural network (EMD-NN) models was applied to two real data sets from two different time periods. The one-period ahead forecasts for both time periods show that EMD-NN outperforms the classical NN model and seasonal ARIMA. In addition, the findings also indicate that EMD-NN can significantly improve forecasting performance during the periods in which macroeconomic conditions are more volatile.

The pandemic had a huge negative impact globally on small and micro firms, particularly on cultural enterprises, making it imperative for them to create strategic solutions for sustainable business models and customer relationships. This chapter studies the digital interventions employed by the micro cultural enterprises in the Japanese Onsens (Hot baths) sector during the pandemic period in Japan. Using the theoretical lenses of service dominant logic and value creation, the study extracts four prominent value creation processes from the analysis of the employed secondary data. The study underlines the importance of collaboration between a firm's internal and external resources, their creative use of operant resources, and a robust customer orientation leading to creative digitalization. The results of the study show how cultural enterprises can rethink customer service in the cultural and creative sector. It also draws attention to the need for more robust policies and support systems that can encourage global cultural enterprises to develop sustainable business models.

This study examines the volatility of inflation in Indonesia before and during COVID-19, focusing on people’s purchasing power. The high inflation variability makes future price expectations uncertain, creating risks in the long run and uncertainty in wealth redistribution. The ARIMA model was used from January 2005 to June 2020. The results show that the ARMA (0.1) model is suitable for testing inflation volatility in Indonesia. Forecasting results show that inflation for the next six months will still be under pressure due to COVID-19.

Commercial real estate and office rental values, in particular, have long been the focus of research. Several forecasting frameworks for office rental values in multivariate and univariate fashions have been proposed. Recent developments in time series forecasting using machine learning and deep learning methods offer an opportunity to update traditional univariate forecasting frameworks.

With the aim to extend research on univariate rent forecasting a hybrid methodology combining both ARIMA and a neural network model is proposed to exploit the unique strengths of both methods in linear and nonlinear modelling. N-BEATS, a deep learning algorithm that has demonstrated state-of-the-art forecasting performance in major forecasting competitions, are explained. With the ARIMA model, it is jointly applied to the office rental dataset to produce forecasts for four-quarters ahead.

When the approach is applied to a dataset of 21 major European office cities, the results show that the ensemble model can be an effective approach to improve the prediction accuracy achieved by each of the models used separately.

Real estate forecasting is essential for assessing the value of managing portfolios and for evaluating investment strategies. The approach applied in this paper confirms the heterogeneity of real estate markets. The application of mixed modelling via linear and nonlinear methods decreases the uncertainty of abrupt changes in rents.

To the best of the authors' knowledge, no such application of a hybrid model updating classical statistical forecasting with a deep learning neural network approach in the field of commercial real estate rent forecasting has been undertaken.