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Fluctuations in the tender price index have an adverse effect on the construction sector and the economy at large. This is largely due to the positive relationship that exists between the construction industry and economic growth. The consequences of these variations include cost overruns and schedule delays, among others. An accurate forecast of the tender price index is good for controlling the uncertainty associated with its variation. In the present study, the efficacy of using an adaptive neuro-fuzzy inference system (ANFIS) for tender price forecasting is investigated. In addition, the Box–Jenkins model, which is considered a benchmark technique, was used to evaluate the performance of the ANFIS model. The results demonstrate that the ANFIS model is superior to the Box–Jenkins model in terms of the accuracy and reliability of the forecast. The ANFIS could provide an accurate and reliable forecast of the tender price index in the medium term (i.e. over a three-year period). This chapter provides evidence of the advantages of applying nonlinear modelling techniques (such as the ANFIS) to tender price index forecasting. Although the proposed ANFIS model is applied to the tender price index in this study, it can also be applied to a wider range of problems in the field of construction engineering and management.

This paper discusses the use of stochastic models based on the Box‐Jenkins modeling methodology to determine the future electrical loads. The developed forecasting models have been applied successfully by using the electrical load data provided by the Oklahoma Gas and Electric Company.

This study/paper aims to present a separable identification algorithm for a multiple input single output (MISO) continuous time (CT) hybrid “Box–Jenkins”.

This paper proposes an optimal method for the identification of MISO CT hybrid “Box–Jenkins” systems with unknown time delays by using the two-stage recursive least-square (TS-RLS) identification algorithm.

The effectiveness of the proposed scheme is shown with application to a simulation example.

A two-stage recursive least-square identification method is developed for multiple input single output continuous time hybrid “Box–Jenkins” system with multiple unknown time delays from sampled data. The proposed technique allows the division of the global CT hybrid “Box–Jenkins” system into two fictitious subsystems: the first one contains the parameters of the system model, including the multiple unknown time delays, and the second contains the parameters of the noise model. Then the TS-RLS identification algorithm can be applied easily to estimate all the parameters of the studied system.

The purpose of this paper is to study the identification methods for multivariable nonlinear Box‐Jenkins systems with autoregressive moving average (ARMA) noises, based on the auxiliary model and the multi‐innovation identification theory.

A multi‐innovation generalized extended least squares (MI‐GELS) and a multi‐innovation generalized ex‐tended stochastic gradient (MI‐GESG) algorithms are developed for multivariable nonlinear Box‐Jenkins systems based on the auxiliary model. The basic idea is to construct an auxiliary model from the measured data and to replace the unknown terms in the information vector with their estimates (i.e. the outputs of the auxiliary model).

It is found that the proposed algorithms can give high accurate parameter estimation compared with existing stochastic gradient algorithm and recursive extended least squares algorithm.

In this paper, the AM‐MI‐GESG and AM‐MI‐GELS algorithms for MIMO Box‐Jenkins systems with nonlinear input are presented using the multi‐innovation identification theory and the proposed algorithms can improve the parameter estimation accuracy. The paper provides a simulation example.

The rapid urbanization of Indian cities and the population surge in cities has steered a massive demand for energy, thereby increasing the carbon emissions in the environment. Information and technology advancements, aided by predictive tools, can optimize this energy demand and help reduce harmful carbon emissions. Out of the multiple factors governing the energy consumption and comfort of buildings, indoor room temperature is a critical one, as it envisages the need for regulating the temperature. This paper aims to propose a mathematical model for short-term forecasting of indoor room temperature in the Indian context to optimize energy consumption and reduce carbon emissions in the environment.

A study is conducted to forecast the indoor room temperature of an Indian corporate building structure, based upon various external environmental factors: temperature and rainfall and internal factors like cooling control, occupancy behavior and building characteristics. Expert insight and principal component analysis are applied for appropriate variables selection. The machine learning approach using Box–Jenkins time series models is used for the forecasting of indoor room temperature.

ARIMAX model, with lagged forecasted and explanatory variables, is found to be the best-fit model. A predictive short-term hourly temperature forecasting model is developed based upon ARIMAX model, which yields fairly accurate results for data set pertaining to the building conditions and climatic parameters in the Indian context. Results also investigate the relationships between the forecasted and individual explanatory variables, which are validated using theoretical proofs.

The models considered in this research are Box–Jenkins models, which are linear time series models. There are non-linear models, such as artificial neural network models and deep learning models, which can be a part of this study. The study of hybrid models including combined forecasting techniques comprising linear and non-linear methods is another important area for future scope of study. As this study is based on a single corporate entity, the models developed need to be tested further for robustness and reliability.

Forecasting of indoor room temperature provides essential practical information about meeting the in-future energy demand, that is, how much energy resources would be needed to maintain the equilibrium between energy consumption and building comfort. In addition, this forecast provides information about the prospective peak usage of air-conditioning controls within the building indoor control management system through a feedback control loop. The resultant model developed can be adopted for smart buildings within Indian context.

This study has been conducted in India, which has seen a rapid surge in population growth and urbanization. Being a developing country, India needs to channelize its energy needs judiciously by minimizing the energy wastage and reducing carbon emissions. This study proposes certain pre-emptive measures that help in minimizing the consumption of available energy resources as well as reducing carbon emissions that have significant impact on the society and environment at large.

A large number of factors affecting the indoor room temperature present a research challenge for model building. The paper statistically identifies the parameters influencing the indoor room temperature forecasting and their relationship with the forecasted model. Considering Indian climatic, geographical and building structure conditions, the paper presents a systematic mathematical model to forecast hourly indoor room temperature for next 120 h with fair degree of accuracy.

The purpose of this study is to contribute to the relatively narrow existing residential real estate literature by developing and validating several univariate forecasting models, to reliably anticipate future house price dynamics across several European Union (EU) countries.

The research approach relies on the time series analysis, by using the Box–Jenkins autoregressive integrated moving average (ARIMA) methodology to explore the trends of residential property prices in selected EU countries and to obtain a snapshot of the potential signs of change to be witnessed by domestic residential markets on a short time-period. The analysis has been performed distinctly for each country in the sample, to account for country-specific past and future trends as well as similarities in their house price growth rate evolutions. The models were estimated for a broad sample of quarterly observations during 1990-2015, while the forecast horizon ranged between the third quarter of 2015 and the fourth quarter of 2016.

The findings suggested that residential property prices’ real growth rate can be modeled through the Box–Jenkins method for France, The Netherlands, Sweden and UK. The pattern of Italy’s residential property prices’ real growth rate cannot be explained by means of univariate ARIMA models, being more suited for multivariate models.

The article subscribes to the need for timely, high-frequency and quality data about house price trends in Europe, to increase the accuracy of forecasts and prevent the appearance of bubbles on real estate market. It compares residential property prices’ dynamics across European countries to identify housing markets with similar patterns of their prices.

It is important to forecast index series to identify future rises, falls, and turning points in the property market. From the point of this necessity and importance, the main purpose of this paper is to forecast the future trends in Dubai housing market.

This paper uses the monthly time series of Reidin.com Dubai Residential Property Price Index (DRPPI) data. In order to forecast the future trends in Dubai housing market, Box‐Jenkins autoregressive integrated moving average (ARIMA) forecasting method is utilized.

The results of the ARIMA modeling clearly indicate that average monthly percentage increase in the Reidin.com DRPPI will be 0.23 percent during the period January 2011‐December 2011. That is a 2.44 percent increase in the index for the same period.

Reidin.com residential property price index is a crucial tool to measure Dubai's real estate market. Based on the current index values or past trend, real estate investors (i.e. developers and constructors) decide to start new projects. Attempts have also been made in the past to forecast index series to identify future rises, falls, and turning points in the property market. The results of this paper would also help government and property investors for creating more effective property management strategies in Dubai.

There is no previous study analyzing the future trends in Dubai housing market. At this point, the paper is the first academic study that identifies this relationship.

A simulation study of the importance of the choice of demand forecasting method in the aggregate capacity planning of the UK electricity supply industry is reported. Using a financial performance measure rather than the conventional measures of accuracy, some of the univariate or extrapolative forecasting methods employed were found to perform surprisingly well over a six year time horizon, suggesting that such methods may merit a greater role in aggregate capacity planning than they appear to be accorded in current practice.

The importance of accurate forecasts of tourism demand for managerial decision making is widely recognized (see, for example, Archer 1987), and this study examines the literature on the accuracy of tourism forecasts generated by different forecasting techniques. In fact, although there are many possible forecasting methods, in practice relatively few of these have been used for tourism forecasting.

It is widely believed that the construction industry is more volatile than other sectors of the economy. Accurate predictions of the level of aggregate demand for construction are of vital importance to all sectors of this industry (e.g. developers, builders and consultants). Empirical studies have shown that accuracy performance varies according to the type of forecasting technique and the variable to be forecast. Hence, there is a need to gain useful insights into how different techniques perform, in terms of accuracy, in the prediction of demand for construction. In Singapore, the residential sector has often been regarded as one of the most important owing to its large percentage share in the total value of construction contracts awarded per year. In view of this, there is an increasing need to objectively identify a forecasting technique which can produce accurate demand forecasts for this vital sector of the economy. The three techniques examined in the present study are the univariate Box‐Jenkins approach, the multiple loglinear regression and artificial neural networks. A comparison of the accuracy of the demand models developed shows that the artificial neural network model performs best overall. The univariate Box‐Jenkins model is the next best, while the multiple loglinear regression model is the least accurate. Relative measures of forecasting accuracy dealing with percentage errors are used to compare the forecasting accuracy of the three different techniques.