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Classical unit root tests are known to suffer from potentially crippling size distortions, and a range of procedures have been proposed to attenuate this problem, including the use of bootstrap procedures. It is also known that the estimating equation’s functional form can affect the outcome of the test, and various model selection procedures have been proposed to overcome this limitation. In this chapter, the authors adopt a model averaging procedure to deal with model uncertainty at the testing stage. In addition, the authors leverage an automatic model-free dependent bootstrap procedure where the null is imposed by simple differencing (the block length is automatically determined using recent developments for bootstrapping dependent processes). Monte Carlo simulations indicate that this approach exhibits the lowest size distortions among its peers in settings that confound existing approaches, while it has superior power relative to those peers whose size distortions do not preclude their general use. The proposed approach is fully automatic, and there are no nuisance parameters that have to be set by the user, which ought to appeal to practitioners.

Motivated by empirical features that characterize cryptocurrency volatility data, the authors develop a forecasting strategy that can account for both model uncertainty and heteroskedasticity of unknown form. The theoretical investigation establishes the asymptotic optimality of the proposed heteroskedastic model averaging heterogeneous autoregressive (H-MAHAR) estimator under mild conditions. The authors additionally examine the convergence rate of the estimated weights of the proposed H-MAHAR estimator. This analysis sheds new light on the asymptotic properties of the least squares model averaging estimator under alternative complicated data generating processes (DGPs). To examine the performance of the H-MAHAR estimator, the authors conduct an out-of-sample forecasting application involving 22 different cryptocurrency assets. The results emphasize the importance of accounting for both model uncertainty and heteroskedasticity in practice.

This research developed a model to understand and predict housing market dynamics and evaluate the significance of house permits data in the model’s forecasting capability.

The research uses a multilevel algorithm consisting of a machine-learning regression model to predict the independent variables and another regressor to predict the dependent variable using the forecasted independent variables.

The research establishes a statistically significant relationship between housing permits and house prices. The novel approach discussed in this paper has significantly higher prediction capabilities than a traditional regression model in forecasting monthly average prices (R-squared value: 0.5993), house price index prices (R-squared value: 0.99) and house sales prices (R-squared value: 0.7839).

The impact of supply, demand and socioeconomic factors will differ in various regions. The forecasting capability and significance of the independent variables can vary, but the methodology can still be applicable when provided with the considered variables in the model.

The resulting model is helpful in the decision-making process for investments, house purchases and construction as the housing demand increases across various cities. The methodology can benefit multiple players, including the government, real estate investors, homebuyers and construction companies.

Existing algorithms and models do not consider the number of new house constructions, monthly sales and inventory in the real estate market, especially in the United States. This research aims to address these shortcomings using current socioeconomic indicators, permits, monthly real estate data and population information to predict house prices and inventory.

The Chinese housing market has gone through rapid growth during the past decade, and house price forecasting has evolved to be a significant issue that draws enormous attention from investors, policy makers and researchers. This study investigates neural networks for composite property price index forecasting from ten major Chinese cities for the period of July 2005–April 2021.

The goal is to build simple and accurate neural network models that contribute to pure technical forecasts of composite property prices. To facilitate the analysis, the authors consider different model settings across algorithms, delays, hidden neurons and data spitting ratios.

The authors arrive at a pretty simple neural network with six delays and three hidden neurons, which generates rather stable performance of average relative root mean square errors across the ten cities below 1% for the training, validation and testing phases.

Results here could be utilized on a standalone basis or combined with fundamental forecasts to help form perspectives of composite property price trends and conduct policy analysis.

This study aims to propose a risk-induced game theoretic forecasting model to predict average daily rate (ADR) under COVID-19, using an advanced recurrent neural network.

Using three data sets from upper-midscale hotels in three locations (i.e. urban, interstate and suburb), from January 1, 2018, to August 31, 2020, three long-term, short-term memory (LSTM) models were evaluated against five traditional forecasting models.

The models proposed in this study outperform traditional methods, such that the simplest LSTM model is more accurate than most of the benchmark models in two of the three tested hotels. In particular, the results show that traditional methods are inefficient in hotels with rapid fluctuations of demand and ADR, as observed during the pandemic. In contrast, LSTM models perform more accurately for these hotels.

This study is limited by its use of American data and data from midscale hotels as well as only predicting ADR.

This study produced a reliable, accurate forecasting model considering risk and competitor behavior.

This paper extends the application of game theory principles to ADR forecasting and combines it with the concept of risk for forecasting during uncertain times.

This study is the first study, to the best of the authors’ knowledge, to use actual hotel data from the COVID-19 pandemic to determine an appropriate neural network forecasting method for times of uncertainty. The application of Shapley value and operational risk obtained a game-theoretic property-level model, which fits best.

The Chinese housing market has witnessed rapid growth during the past decade and the significance of housing price forecasting has undoubtedly elevated, becoming an important issue to investors and policymakers. This study aims to examine neural networks (NNs) for office property price index forecasting from 10 major Chinese cities for July 2005–April 2021.

The authors aim at building simple and accurate NNs to contribute to pure technical forecasts of the Chinese office property market. To facilitate the analysis, the authors explore different model settings over algorithms, delays, hidden neurons and data-spitting ratios.

The authors reach a simple NN with three delays and three hidden neurons, which leads to stable performance of about 1.45% average relative root mean square error across the 10 cities for the training, validation and testing phases.

The results could be used on a standalone basis or combined with fundamental forecasts to form perspectives of office property price trends and conduct policy analysis.

The purpose of this paper is to carry out a detailed analysis of two port converter fed by Solar and wind sources during different operational modes by small signal modelling. The converter is fully characterized and simulated using Matlab/Simulink. The voltage and current waveforms along with their corresponding expressions describing the converter operation are presented in detail. Then the DC-averaged equivalent topology is derived using circuit averaging technique. A complete derivation of the power stage transfer functions relevant to the capacitor voltage loop, such as capacitor voltage to solar voltage and inductor current to wind input voltage is obtained.

Stability analysis is used to analyze the small deviations around the steady-state operating point which helps in modeling the closed loop converter parameters. This paper presents the analysis, modeling and control of two port Cuk-buck converter topology.

Based on the results, a control strategy is designed to manage the energy flow within the system. A lab-level prototype for Cuk-buck converter with PWM controller is implemented and tested under various input conditions to study the performance of the converter during seasonal changes. The simulation and experimental results showed that effective operation and control strategy of the hybrid power supply system managed to be achieved alongside its feasible outputs.

This analysis can be extended to all power electronic converters and will be useful for the design of controllers.

An appropriate control design plays a key role in enhancing the overall performance of the system. Hence, this paper is intended to present in detail the small signal modeling of the Cuk-buck converter along with the control design for all the switching modes.

Though this type of converter topology has been discussed widely in literature, very scarce literature is available related to modeling and control design of the converter. A state-space averaging model of the converter followed by a type-II compensator design is described, and prototype design and experimental results are also presented.

This research navigates the confluence of data analytics, machine learning, and artificial intelligence to revolutionize the management of urban services in smart cities. The study thoroughly investigated with advanced tools to scrutinize key performance indicators integral to the functioning of smart cities, thereby enhancing leadership and decision-making strategies. Our work involves the implementation of various machine learning models such as Logistic Regression, Support Vector Machine, Decision Tree, Naive Bayes, and Artificial Neural Networks (ANN), to the data. Notably, the Support Vector Machine and Bernoulli Naive Bayes models exhibit robust performance with an accuracy rate of 70% precision score. In particular, the study underscores the employment of an ANN model on our existing dataset, optimized using the Adam optimizer. Although the model yields an overall accuracy of 61% and a precision score of 58%, implying correct predictions for the positive class 58% of the time, a comprehensive performance assessment using the Area Under the Receiver Operating Characteristic Curve (AUC-ROC) metrics was necessary. This evaluation results in a score of 0.475 at a threshold of 0.5, indicating that there's room for model enhancement. These models and their performance metrics serve as a key cog in our data analytics pipeline, providing decision-makers and city leaders with actionable insights that can steer urban service management decisions. Through real-time data availability and intuitive visualization dashboards, these leaders can promptly comprehend the current state of their services, pinpoint areas requiring improvement, and make informed decisions to bolster these services. This research illuminates the potential for data analytics, machine learning, and AI to significantly upgrade urban service management in smart cities, fostering sustainable and livable communities. Moreover, our findings contribute valuable knowledge to other cities aiming to adopt similar strategies, thus aiding the continued development of smart cities globally.

Industry 4.0 has offered significant potential for manufacturing firms to alter and rethink their business models, production processes, strategies and objectives. Manufacturing organizations have recently undergone substantial transformation due to Industry 4.0 technologies. Hence, to successfully deploy and embed Industry 4.0 technologies in their organizational operations and practices, businesses must assess their adoption readiness. For this purpose, a multi-dimensional analytical indicator methodology has been developed to measure Industry 4.0 maturity and preparedness.

A weighted average method was adopted to assess the Industry 4.0 readiness using a case study from a steel manufacturing organization.

The result revealed that the firm ranks between Industry 2.0 and Industry 3.0, with an overall score of 2.32. This means that the organization is yet to achieve Industry 4.0 mature and ready organization.

The multi-dimensional indicator framework proposed can be used by managers, policymakers, practitioners and researchers to assess the current status of organizations in terms of Industry 4.0 maturity and readiness as well as undertake a practical diagnosis and prognosis of systems and processes for its future adoption.

Although research on Industry 4.0 maturity models has grown exponentially in recent years, this study is the first to develop a multi-dimensional analytical indicator to measure Industry 4.0 maturity and readiness.