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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 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.

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.

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.

The purpose of this paper is to examine the performance of the vector error‐correction (VEC) econometric modelling technique in predicting short‐ to medium‐term construction manpower demand.

The VEC modelling technique is evaluated with two conventional forecasting methods: the Box‐Jenkins approach and the multiple regression analysis, based on the forecasting accuracy on construction manpower demand.

While the forecasting reliability of the VEC modelling technique is slightly inferior to the multiple log‐linear regression analysis in terms of forecasting accuracy, the error correction econometric modelling technique outperformed the Box‐Jenkins approach. The VEC and the multiple linear regression analysis in forecasting can better capture the causal relationship between the construction manpower demand and the associated factors.

Accurate predictions of the level of manpower demand are important for the formulation of successful policy to minimise possible future skill mismatch.

The accuracy of econometric modelling technique has not been evaluated empirically in construction manpower forecasting. This paper unveils the predictability of the prevailing manpower demand forecasting modelling techniques. Additionally, economic indicators that are significantly related to construction manpower demand are identified to facilitate human resource planning, and policy simulation and formulation in construction.

The term “economic growth” refers to the increase of real gross national product or gross domestic product or per capita income. National income or else national product is usually expressed as a measure of total added value of a domestic economy known as gross domestic product (GDP). Generally, GDP measures the value of economic activity within a country during a specific time period. The current study aims to find the most suitable model that adjusts on a time-series data set using Box-Jenkins methodology and to examine the forecasting ability of this model. The analysis used quarterly data for Greece from the first quarter of 1995 until the third quarter of 2019. Nonlinear maximum likelihood estimation (maximum likelihood-ML) was applied to estimate the model using the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm while covariance matrix was estimated using the negative of the matrix of log-likelihood second derivatives (Hessian-observed). Forecasting of the time series was achieved both with dynamic as well as static procedures using all forecasting criteria.

This paper focuses on assembly line performance of an automotive body shop that builds body‐in‐white (BIW) assembly utilizing about 700+ process robots. These robots perform various operations such as welding, sealing, part handling, stud welding and inspection. There is no accurate tool available for the plant personnel to predict the future throughput based on plant's data. The purpose of this paper is to provide future throughput performance prediction based on plant data using Box‐Jenkins' ARMA model.

The following data were collected for five major assembly lines. First, the assembly machine‐in‐cycle time: the assembly line machines include robots that perform various functions like load, welding or sealing and unloading parts; the manual operators loading cycle time to the production fixtures. The conveyors act as buffers in between stations, and also feed to the production cells, and carry parts from station to station. The conveyors' downtime and uptime were also part of the machine‐in‐cycle time; second, the number of units produced from the beginning to the end of the assembly line; third, the number of fault occurrences in the assembly line due to various machine breakdowns; fourth, the machine availability percentage – i.e. the machine is readily available to perform its functions (the machine blocked upstream (starving) and blocked down (downstream) state is considered here); fifth, the actual efficiency of the machine measured in percentage based on output percentage; sixth, the expected number of units at designed efficiency.

In summary, this research paper provided a systematic development of a forecast model based on Box‐Jenkin's ARMA methodology to analyze the complex assembly line process performance data. The developed ARMA forecast models proved that the future prediction can be accurately predicted based on the past plant performance data. The developed ARMA forecast models predicted the future throughput performance within 99.52 percent accuracy. The research findings were validated by the actual plant performance data.

In this study, the automotive assembly process machines (robots, conveyors and fixtures) production data were collected, statistically analyzed and verified for viable ARMA model verification. The verified ARMA model has been used to predict the plant future months' throughput with 99.52 percent accuracy, based on the plant production data. This research is unique because of its practical usage to improve production.

The study attempts to examine the effect of the COVID-19 pandemic on the economic growth and public debt of the Indian economy. The authors also attempt to make quarterly projections of economic growth and external debt (ED) for the next five years. The objective is to understand how much time the economy takes to recover and at what pace. Consequently, this study elucidates the composition of debt after the crisis in the next five years.

To predict India's gross domestic product (GDP) and ED for the next five years, the authors used an auto-regressive integrated moving average (ARIMA) model. This model was built under a Box–Jenkins methodology (Box and Jenkins, 1976) and was subjected to an augmented Dickey–Fuller (ADF) test to check the stationarity of the data. The methodology includes three main steps to estimate and forecast the model: identification, estimation, and diagnostic and forecasting.

The study finds that the outbreak of the COVID-19 pandemic has significant implications for economic growth and public debt. The economy faced contraction in the first quarter of the year 2020 due to the suspension of economic activities and still struggling with the negative values of GDP. The forecasting results reveal that ED will continue to grow to meet the increasing health expenditure needs, and GDP will also bounce back slowly after the end of the year 2021. It has been noticed that the recurrent crisis derails the developing economies from the path of sustainable development to a prolonged economic slump with mounting public debt.

The study examines the impact of the COVID-19 pandemic on economic growth and public debt with particular reference to India. To the best of the authors’ knowledge, this is the first time the quarterly projections for GDP and ED have been made after the COVID-19 crisis.

The authors derive the probability distribution of the net present value of a project under the quite general assumption that the cash flows follow either an autoregressive moving average process or an integrated autoregressive process. Examples are presented which serve to both illustrate the application of the results as well as to underscore how to use utility functions for decision making, how to determine a project's Internal Rate of Return, and the dynamic resolution of uncertainty.