Role In Forecasting Research Articles

The Role of Forecasts in Planning for Energy Infrastructure: A Historical Look at Past Futures in Postwar Quebec

The Role of Forecasts in Planning for Energy Infrastructure: A Historical Look at Past Futures in Postwar Quebec

THE ROLE OF TOURIST RESOURCES IN THE ACTIVITIES OF TOURIST ENTERPRISES

In the article, the strategies of tourism enterprises for the future period, the ways of effective use of the existing opportunities of the tourism infrastructure are examined. Factors hindering the sustainable development of the tourism sector in Azerbaijan: high prices in air transportation and lack of adequate accommodation were analyzed and solutions were proposed. Also, relevant proposals were made in the direction of the economic assessment of the current tourism potential in Azerbaijan, the effective use of tourism resources to have a positive effect on the socio-economic development of the region in general. As a result of the research, it was determined that in the current conditions, if the potential of tourism resources in Azerbaijan is used effectively, important progress can be made for the future period, and the tourism industry will have a major share in the country’s economy. The purpose – Forecasting the future activity of tourism enterprises in Azerbaijan consists of investigating ways of efficient use for the future period. Methodology – economic, statistical and comparative analysis methods were used in the research work. The results of the study – the role of forecasts in the activity of tourism institutions is to determine the positive and negative aspects that affect their future development. Corresponding contributions to the elimination of the identified problems have been noted in this direction.

The Role of Forecasting and Planning Management in Sustainable Hospital Supply Chain

In the continually evolving landscape of healthcare services, sustainable hospital supply chain management (SCM) has become a focal point in driving efficiency and improving patient care. This paper builds upon previous discussions about the critical roles of Hospital Infrastructure Development (HID) and Collaboration & Information Flow (CIF) in enhancing sustainable SCM. Herein, the significance of Forecasting and Planning Management (FPM) is examined as an essential component for sustainable SCM in the healthcare setting.

When Do Associate Analysts Matter?

Although extant literature investigates the role of sell-side equity analysts in capital markets, most studies do not consider that sell-side equity analysts often work in hierarchical teams. Lead analysts manage a team of associate and junior analysts who participate in the team’s tasks. Building on the delegation theory in the management literature, we hypothesize and find a division of labor between lead and associate analysts, where lead analysts are more likely to delegate tasks (1) that are less significant, (2) that are simpler, (3) when the workload of the lead analyst increases, and (4) when the associate analyst is more competent. Our results further suggest that associate analysts play a significant role in forecasting. By contrast, lead analysts are the main contributors to the qualitative aspects of analyst reports and are more likely to participate in earnings conference calls. Overall, our study documents the significant role of associate analysts in forecasting and the division of labor between lead and associate analysts. This paper was accepted by Brian Bushee, accounting.

Developing a Novel Continuous Metabolic Syndrome Score: A Data Mining Based Model

Today, Metabolic Syndrome in the age group of children and adolescents has become a global concern. In this paper, a data mining model is used to determine a continuous Metabolic Syndrome (cMetS) score using Linear Discriminate Analysis (cMetS-LDA). The decision tree model is used to specify the calculated optimal cut-off point cMetS-LDA. In order to evaluate the method, multilayer perceptron neural network (NN) and Support Vector Machine (SVM) models were used and statistical significance of the results was tested with Wilcoxon signed-rank test. According to the results of this test, the proposed CART is significantly better than the NN and SVM models. The ranking results in this study showed that the most important risk factors in making cMetS-LDA were WC, SBP, HDL and TG for males and WC, TG, HDL and SBP for females. Our research results show that high TG and central obesity have the greatest impact on MetS and FBS has no effect on the final prognosis. The results also indicate that in the preliminary stages of MetS, WC, HDL and SBP are the most important influencing factors that play an important role in forecasting.

Quantitative measure of memory loss in complex spatiotemporal systems.

History dependence of the evolution of complex systems plays an important role in forecasting. The precision of the predictions declines as the memory of the systems is lost. We propose a simple method for assessing the rate of memory loss that can be applied to experimental data observed in any metric space X. This rate indicates how fast the future states become independent of the initial condition. Under certain regularity conditions on the invariant measure of the dynamical system, we prove that our method provides an upper bound on the mixing rate of the system. This rate can be used to infer the longest time scale on which the predictions are still meaningful. We employ our method to analyze the memory loss of a slowly sheared granular system with a small inertial number I. We show that, even if I is kept fixed, the rate of memory loss depends erratically on the shear rate. Our study suggests the presence of bifurcations at which the rate of memory loss increases with the shear rate, while it decreases away from these points. We also find that the rate of memory loss is closely related to the frequency of the sudden transitions of the force network. Moreover, the rate of memory loss is also well correlated with the loss of correlation of shear stress measured at the system scale. Thus, we have established a direct link between the evolution of force networks and the macroscopic properties of the considered system.

Forecasting Models for Coronavirus Disease (COVID-19): A Survey of the State-of-the-Art.

COVID-19 is a pandemic that has affected over 170 countries around the world. The number of infected and deceased patients has been increasing at an alarming rate in almost all the affected nations. Forecasting techniques can be inculcated thereby assisting in designing better strategies and in taking productive decisions. These techniques assess the situations of the past thereby enabling better predictions about the situation to occur in the future. These predictions might help to prepare against possible threats and consequences. Forecasting techniques play a very important role in yielding accurate predictions. This study categorizes forecasting techniques into two types, namely, stochastic theory mathematical models and data science/machine learning techniques. Data collected from various platforms also play a vital role in forecasting. In this study, two categories of datasets have been discussed, i.e., big data accessed from World Health Organization/National databases and data from a social media communication. Forecasting of a pandemic can be done based on various parameters such as the impact of environmental factors, incubation period, the impact of quarantine, age, gender and many more. These techniques and parameters used for forecasting are extensively studied in this work. However, forecasting techniques come with their own set of challenges (technical and generic). This study discusses these challenges and also provides a set of recommendations for the people who are currently fighting the global COVID-19 pandemic.

Common Factor Augmented Forecasting Models for the US Dollar-Korean Won Exchange Rate

We propose factor-augmented out of sample forecasting models for the real exchange rate between Korea and the US. We estimate latent common factors by applying an array of data dimensionality reduction methods to a large panel of monthly frequency time series data. We augment benchmark forecasting models with common factor estimates to formulate out-of-sample forecasts of the real exchange rate. Major findings are as follows. First, our factor models outperform conventional forecasting models when combined with factors from the US macroeconomic predictors. Second, our factor models perform well at longer horizons when American real activity factors are employed, whereas American nominal/financial market factors help improve short-run prediction accuracy. Third, models with global PLS factors from UIP fundamentals overall perform well, while PPP and RIRP factors play a limited role in forecasting.

Short-Term Traffic Flow Forecasting via Multi-Regime Modeling and Ensemble Learning

Short-term traffic flow forecasting is crucial for proactive traffic management and control. One key issue associated with the task is how to properly define and capture the temporal patterns of traffic flow. A feasible solution is to design a multi-regime strategy. In this paper, an effective approach to forecasting short-term traffic flow based on multi-regime modeling and ensemble learning is presented. First, to properly capture the different patterns of traffic flow dynamics, a regime identification model based on probabilistic modeling was developed. Each identified regime represents a specific traffic phase, and was used as the representative feature for the forecasting modeling. Second, a forecasting model built on an ensemble learning strategy was developed, which integrates the forecasts of multiple regression trees. The traffic flow data over 5-min intervals collected from four I-80 freeway segments, in California, USA, was used to evaluate the proposed approach. The experimental results show that the identified regimes are able to well explain the different traffic phases, and play an important role in forecasting. Furthermore, the developed forecasting model outperformed four typical models in terms of root mean square error (RMSE) and mean absolute percentage error (MAPE) on three traffic flow measures.

FORECASTING AND COPING WITH FUTURE STRESSORS PREDICTS DAILY SUBJECTIVE AGE

Stressors are associated with higher subjective ages, but the role of forecasting and coping with future stressors is unknown. 223 adults (107 aged 18-36, 116 aged 60-90) reported their subjective age, forecasts of next-day health stressors, and anticipatory coping with next-day health stressors each day for eight consecutive days. There was no main effect of forecasting, but increases in plan rehearsal coping were associated with increases in felt age. In contrast, increases in problem analysis coping were associated with decreases in felt age. Daily forecasting and coping also interacted with each other. On days with low plan rehearsal or low problem analysis, there was no association between forecasting of health stressors and subjective age. However, on days with high plan rehearsal or high problem analysis, increases in forecasting ratings were associated with increases in subjective age. Forecasting and coping with future stressors may play a role in subjective aging.

Do Associate Analysts Matter?

While extant literature investigates the role of sell-side equity analysts in capital markets, most studies do not consider that sell-side equity analysts often work in hierarchical teams. Lead analysts manage a team of associate and junior analysts who participate in the team’s tasks. Building on the delegation theory in the management literature, we hypothesize and find a division of labor between lead and associate analysts, where lead analysts are more likely to delegate tasks that are (1) less significant and (2) simpler, and when (3) the workload of the lead analyst increases, and (4) the associate analyst is more competent. Our results further suggest that associate analysts play a significant role in forecasting. In contrast, lead analysts are the main contributors to the qualitative aspects of analyst reports and are more likely to participate in earnings conference calls. Overall, our study documents the significant role of associate analysts in forecasting and the division of labor between lead and associate analysts.

How numbers of the future are shaping today: The role of forecasts and calculations in public sector strategic thinking

AbstractThis article examines the role of numbers in forming a city strategy in the case of Gothenburg, Sweden. The article illustrates how numbers make people act and react and shape the strategy process. The study is situated between the theoretical fields of accounting and strategy processes in cities. By relating them to the concept of ‘governmentality’, numbers are seen as helping render the city governable. Because strategizing is seen as concerning the future, the actors involved in drafting the strategy feel free to challenge historically institutionalized practices. The effect is paradoxical: the future‐looking strategy process becomes a forum for solving current problems.

Advancing Polar Prediction Capabilities on Daily to Seasonal Time Scales

Abstract The polar regions have been attracting more and more attention in recent years, fueled by the perceptible impacts of anthropogenic climate change. Polar climate change provides new opportunities, such as shorter shipping routes between Europe and East Asia, but also new risks such as the potential for industrial accidents or emergencies in ice-covered seas. Here, it is argued that environmental prediction systems for the polar regions are less developed than elsewhere. There are many reasons for this situation, including the polar regions being (historically) lower priority, with fewer in situ observations, and with numerous local physical processes that are less well represented by models. By contrasting the relative importance of different physical processes in polar and lower latitudes, the need for a dedicated polar prediction effort is illustrated. Research priorities are identified that will help to advance environmental polar prediction capabilities. Examples include an improvement of the polar observing system; the use of coupled atmosphere–sea ice–ocean models, even for short-term prediction; and insight into polar–lower-latitude linkages and their role for forecasting. Given the enormity of some of the challenges ahead, in a harsh and remote environment such as the polar regions, it is argued that rapid progress will only be possible with a coordinated international effort. More specifically, it is proposed to hold a Year of Polar Prediction (YOPP) from mid-2017 to mid-2019 in which the international research and operational forecasting communites will work together with stakeholders in a period of intensive observing, modeling, prediction, verification, user engagement, and educational activities.

Automated driving: The role of forecasts and uncertainty—A control perspective

Driving requires forecasts. Forecasted movements of objects in the driving scene are uncertain. Inevitably, decision and control algorithms for autonomous driving need to cope with such uncertain forecasts. In assisted driving, the uncertainty in the human/vehicle interaction further increases the complexity of the control design task.Our research over the past ten years has focused on control design methods which systematically handle uncertain forecasts for autonomous and semi-autonomous vehicles. This paper presents an overview of our findings and discusses relevant aspects of our recent results.

Precipitation and Temperature Forecast Performance at the Weather Prediction Center

Abstract The role of the human forecaster in improving upon the accuracy of numerical weather prediction is explored using multiyear verification of human-generated short-range precipitation forecasts and medium-range maximum temperature forecasts from the Weather Prediction Center (WPC). Results show that human-generated forecasts improve over raw deterministic model guidance. Over the past two decades, WPC human forecasters achieved a 20%–40% improvement over the North American Mesoscale (NAM) model and the Global Forecast System (GFS) for the 1 in. (25.4 mm) (24 h)−1 threshold for day 1 precipitation forecasts, with a smaller, but statistically significant, 5%–15% improvement over the deterministic ECMWF model. Medium-range maximum temperature forecasts also exhibit statistically significant improvement over GFS model output statistics (MOS), and the improvement has been increasing over the past 5 yr. The quality added by humans for forecasts of high-impact events varies by element and forecast projection, with generally large improvements when the forecaster makes changes ≥8°F (4.4°C) to MOS temperatures. Human improvement over guidance for extreme rainfall events [3 in. (76.2 mm) (24 h)−1] is largest in the short-range forecast. However, human-generated forecasts failed to outperform the most skillful downscaled, bias-corrected ensemble guidance for precipitation and maximum temperature available near the same time as the human-modified forecasts. Thus, as additional downscaled and bias-corrected sensible weather element guidance becomes operationally available, and with the support of near-real-time verification, forecaster training, and tools to guide forecaster interventions, a key test is whether forecasters can learn to make statistically significant improvements over the most skillful of this guidance. Such a test can inform to what degree, and just how quickly, the role of the forecaster changes.

Fuzzy Time Series Forecasting With a Probabilistic Smoothing Hidden Markov Model

Since its emergence, the study of fuzzy time series (FTS) has attracted more attention because of its ability to deal with the uncertainty and vagueness that are often inherent in real-world data resulting from inaccuracies in measurements, incomplete sets of observations, or difficulties in obtaining measurements under uncertain circumstances. The representation of fuzzy relations that are obtained from a fuzzy time series plays a key role in forecasting. Most of the works in the literature use the rule-based representation, which tends to encounter the problem of rule redundancy. A remedial forecasting model was recently proposed in which the relations were established based on the hidden Markov model (HMM). However, its forecasting performance generally deteriorates when encountering more zero probabilities owing to fewer fuzzy relationships that exist in the historical temporal data. This paper thus proposes an enhanced HMM-based forecasting model by developing a novel fuzzy smoothing method to overcome performance deterioration. To deal with uncertainty more appropriately, the roulette-wheel selection approach is applied to probabilistically determine the forecasting result. The effectiveness of the proposed model is validated through real-world forecasting experiments, and performance comparison with other benchmarks is conducted by a Monte Carlo method.

A practical approach for the assessment and illustration of uncertainty in emissions modelling: a case study using GAINS Ireland

Abstract National outlooks of emission levels are important components of international environmental policymaking and associated national policy development. This is the case for both greenhouse gas emissions and transboundary air pollutants. However, there is uncertainty inherent in the production of forecasts. In the climate context, IPCC guidelines have been established to support national teams in quantifying uncertainty within national inventory reporting of historic emissions. These are presented to indicate the potential range of deviation from reported values and to offer added evidence for policy decisions. However, the method and practice of accounting for uncertainty amongst emission forecasts is both less clear and less common. This paper posits that the role of forecasts in setting international targets and planning policy action renders the management of ‘forecast’ uncertainty as important as addressing uncertainty in the context of inventory and compliance work. Failure to explicitly present uncertainty in forecasting delivers an implicit and misplaced confidence in a given future scenario, irrespective of parallel work on other scenarios and sensitivities. However, it is acknowledged that approaches to uncertainty analyses within the literature are often highly technical and the models used are both computationally demanding and time-intensive. This can limit broader adoption where national capacities are limited and scenario development is frequent. This paper describes an approach to presenting uncertainty, where the aim is to balance the technical and temporal demands of uncertainty estimation against a means of delivering regular and practical estimation and presentation of uncertainty for any given scenario. In turn this methodology should help formalise the recognition of the uncertainty dimension in emissions forecasts, for all stakeholders engaged.

Population forecasting: epistemological considerations

In this paper the author reflects on what forecasting is, defined therein as prospective analysis (as opposed to simulation and prediction proper). As such, the paper constitutes a significant departure from the mainstream view of forecasting by shifting the emphasis from the predictability paradigm to the impact paradigm, from forecast as discovering to forecast as creating the future. Today we have access to tools that have a wide range of applicability and are of various degrees of sophistication, depending on the particular future-directed task at hand, and also having a better understanding of the role of forecasting. We are better equipped, technologically and hopefully also cognitively, to manage the future. It is easy to recognize that this author’s stance towards the future — prospects and events that loom on the horizon — is distinctly proactive and not merely submissive or reactive. Though some of the issues raised in this paper have been discussed elsewhere by the author and other writes, the paper offers a coherent, well rounded, synthesis of epistemological assessment of forecasting, and as such is likely to be of interest to both producers and users thereof. Anatole ROMANIUK, Adjunct professor, Ph.D., Department of Sociology, University of Alberta, Canada. E-mail: anromaniuk@rogers.com DOI: 10.4402/genus-153

The impact of spatial elements on the forecasting of Spanish labour series

In this paper, we analyse the ability of a dynamic spatial panel data model without explanatory variables to explain a variable of interest, in this case employment in the fifty Spanish provinces. The best model is a dynamic fixed effect with a spatial lag structure in an equation estimated through the unconditional ML procedure. Predictions derived from this selected model are compared with those derived from fifty seasonal ARIMA models that also treat outlier observations. The results indicate that forecasts derived from a single estimated spatial panel data model are as accurate as those derived from the estimation of fifty seasonal ARIMA models. This shows that spatial panel data models play an important role in forecasting.

TECHNOLOGICAL CULTURE REPERCUSSIONS AND ITS EFFECT ON THE FUTURE OF ARCHITECTURE

Information and automation are considered the most important technologic achievements that form the future lineaments. Also, the breakthrough increating the materials directed forward high performance competence and the using of modern techniques in architecture have changed number of the prevailing concepts in the technical sciences. The future challenges makes it compulsory to review all what is surrounding the architecture process as of theoretical principles and practical procedures on the light of modern concepts of architecture requirements from scientific and technological point of view having impact on the methods of architectural designs . The problem is to be in the challenges facing the architect in finding new architectural language that invest and accompany the increasing progress in the modern techniques; which requires updating the architectural concepts, requirements and tools as well as developing the architect ability to absorb and direct the available data, alternatives and performance styles by making change in the core of teaching and practicing methods of design. The research aims to observe the technological culture Repercussions and its most important features on the activities of architecture ,and the possible stability for the majority of these repercussions and their relation in the future of architecture through the research procedures that include the following axis: - The future as prospective and intellectual approach: It deals with the concept and types of future studies and the role of forecast in the planned change. - Technology as technical and social phenomenon: This deals with the concept of - technology and technicality and the social insight of technology. - Patterns of the contemporary technical change: Deals with the most important technological achievements forms the lineaments of the contemporary, future and technical change such as: - Information technology, digital design, industrial automation norms and new materials technology. - Lineaments of technological culture: deals with information culture and knowledge industry, technical globalization and its repercussions on the social and culture environment, architecture as after modernity and conformity forms with technology age. - The impact of technology on the development of modern attitudes in the design: deals with the forms of the technological impact forms on the modern attitudes of construction and internal design. - The future of architecture education, development of creative notion and the most important characteristics of creative education.