scholarly journals The influence of graphical format on judgmental forecasting accuracy: Lines versus points

2018 ◽  
pp. e7
Author(s):  
Zoe Theocharis ◽  
Leonard A. Smith ◽  
Nigel Harvey
Keyword(s):  
Forecasting Accuracy ◽  
Judgmental Forecasting

The effect of relative performance feedback on judgmental forecasting accuracy

2019 ◽  
Vol 57 (7) ◽  
pp. 1695-1711
Author(s):  
Hyo Young Kim ◽  
Yun Shin Lee ◽  
Duk Bin Jun
Keyword(s):  
Performance Feedback ◽  
Relative Performance ◽  
Human Judgment ◽  
Forecasting Accuracy ◽  
Content Type ◽  
Feedback Information ◽  
Organizational Settings ◽  
Forecasting Performance ◽  
Top Performers ◽  
Judgmental Forecasting

Purpose Forecasting processes in organizational settings largely rely on human judgment, which makes it important to examine ways to improve the accuracy of these judgmental forecasts. The purpose of this paper is to test the effect of providing relative performance feedback on judgmental forecasting accuracy. Design/methodology/approach This paper is based on a controlled laboratory experiment. Findings The authors show that feedback that ranks the forecasting performance of participants improves their accuracy compared with the forecasting accuracy of participants who do not get such feedback. The authors also find that the effectiveness of such relative performance feedback depends on the content of the feedback information as well as on whether accurate forecasting performance is linked to additional financial rewards. Relative performance feedback becomes more effective when subjects are told they rank behind other participants than when they are told they rank higher than other participants. This finding is consistent with loss aversion: low-ranked individuals view their performance as a loss and work harder to avoid it. By contrast, top performers tend to slack off. Finally, the authors find that the addition of monetary rewards for top performers reduces the effectiveness of relative performance feedback, particularly for individuals whose performance ranks near the bottom. Originality/value One way to improve forecasting accuracy when forecasts rely on human judgment is to design an effective incentive system. Despite the crucial role of judgmental forecasts in organizations, little attention has been devoted to this topic. The aim of this study is to add to the literature in this field.

Methods of risk assessment in forecasting the consequences of accidents at pipeline transport facilities

2020 ◽  
Vol 10 (6) ◽  
pp. 663-673
Author(s):  
Vladislav N. Slepnev ◽  
◽  
Alexander F. Maksimenko ◽  
Elena V. Glebova ◽  
Alla Т. Volokhina ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Evaluation Method ◽  
Assessment Procedure ◽  
Expert Evaluation ◽  
Main Pipeline ◽  
Pipeline Transport ◽  
Forecasting Accuracy ◽  
Distribution Of Resources ◽  
Expert Evaluation Method

The choice of risk assessment procedure is one of the essential stages of efficient structuring of processes on prevention, localization and elimination of the consequences of accidents at main pipeline transport facilities. The authors analyzed themed publications and regulatory documents, governing procedures of risk assessment and forecasting of the consequences of possible accidents, and defined main problems in this area. Procedure for the risk assessment of accidents at main pipeline facilities was developed, the basis of which is the expert evaluation method. The procedure includes the determination of the main criteria for the assessment the probability of accident initiation and development and the evaluation of the severity of its consequences, an expert evaluation of criteria significance, their classification, and creation of a rating for hazardous pipeline sections. The application of the procedure application allows to specify the list of facilities that require high priority forecasting of accidents consequences, thus to optimize the distribution of resources and the overall increase of efficiency in planning while defining forces and special technical devices, necessary for containment and rectification of emergencies. Expert evaluation method application allows considering the specifics of certain enterprises, their technical and technological peculiarities, thereby increasing forecasting accuracy.

Improving Demand Forecasting Accuracy for Cultural Facilities Using Gravity Model

2020 ◽  
Vol 24 (2) ◽  
pp. 121-163
Author(s):  
Yun Ju Ham ◽  
Yun Jun Kim ◽  
Geun Seok Hong
Keyword(s):  
Gravity Model ◽  
Demand Forecasting ◽  
Forecasting Accuracy

scholarly journals Short-term load forecasting using Theta method

2019 ◽  
Vol 84 ◽  
pp. 01004 ◽  
Author(s):  
Grzegorz Dudek
Keyword(s):  
Empirical Studies ◽  
Load Forecasting ◽  
Seasonal Cycles ◽  
Short Term ◽  
Forecasting Accuracy ◽  
Forecasting Methods ◽  
Short Term Load Forecasting ◽  
Data Definition ◽  
System Data ◽  
Theta Method

The Theta method attracted the attention of researchers and practitioners in recent years due to its simplicity and superior forecasting accuracy. Its performance has been confirmed by many empirical studies as well as forecasting competitions. In this article the Theta method is tested in short-term load forecasting problem. The load time series expressing multiple seasonal cycles is decomposed in different ways to simplify the forecasting problem. Four variants of input data definition are considered. The standard Theta method is uses as well as the dynamic optimised Theta model proposed recently. The performances of the Theta models are demonstrated through an empirical application using real power system data and compared with other popular forecasting methods.

scholarly journals Load forecasting of refrigerated display cabinet based on CEEMD–IPSO–LSTM combined model

Open Physics ◽  
2021 ◽  
Vol 19 (1) ◽  
pp. 360-374
Author(s):  
Yuan Pei ◽  
Lei Zhenglin ◽  
Zeng Qinghui ◽  
Wu Yixiao ◽  
Lu Yanli ◽  
...  
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Data ◽  
Load Forecasting ◽  
Series Data ◽  
Forecasting Model ◽  
Combined Model ◽  
Forecasting Accuracy ◽  
Forecasting Method ◽  
Consumption Reduction

Abstract The load of the showcase is a nonlinear and unstable time series data, and the traditional forecasting method is not applicable. Deep learning algorithms are introduced to predict the load of the showcase. Based on the CEEMD–IPSO–LSTM combination algorithm, this paper builds a refrigerated display cabinet load forecasting model. Compared with the forecast results of other models, it finally proves that the CEEMD–IPSO–LSTM model has the highest load forecasting accuracy, and the model’s determination coefficient is 0.9105, which is obviously excellent. Compared with other models, the model constructed in this paper can predict the load of showcases, which can provide a reference for energy saving and consumption reduction of display cabinet.

scholarly journals Evolution of disease transmission during the COVID-19 pandemic: patterns and determinants

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jie Zhu ◽  
Blanca Gallego
Keyword(s):  
Public Health ◽  
Disease Transmission ◽  
Clustering Algorithm ◽  
Health Interventions ◽  
Transmission Model ◽  
Model Parameters ◽  
Public Health Interventions ◽  
Forecasting Accuracy ◽  
Time Lagged ◽  
The Impact

AbstractEpidemic models are being used by governments to inform public health strategies to reduce the spread of SARS-CoV-2. They simulate potential scenarios by manipulating model parameters that control processes of disease transmission and recovery. However, the validity of these parameters is challenged by the uncertainty of the impact of public health interventions on disease transmission, and the forecasting accuracy of these models is rarely investigated during an outbreak. We fitted a stochastic transmission model on reported cases, recoveries and deaths associated with SARS-CoV-2 infection across 101 countries. The dynamics of disease transmission was represented in terms of the daily effective reproduction number ($$R_t$$ R t ). The relationship between public health interventions and $$R_t$$ R t was explored, firstly using a hierarchical clustering algorithm on initial $$R_t$$ R t patterns, and secondly computing the time-lagged cross correlation among the daily number of policies implemented, $$R_t$$ R t , and daily incidence counts in subsequent months. The impact of updating $$R_t$$ R t every time a prediction is made on the forecasting accuracy of the model was investigated. We identified 5 groups of countries with distinct transmission patterns during the first 6 months of the pandemic. Early adoption of social distancing measures and a shorter gap between interventions were associated with a reduction on the duration of outbreaks. The lagged correlation analysis revealed that increased policy volume was associated with lower future $$R_t$$ R t (75 days lag), while a lower $$R_t$$ R t was associated with lower future policy volume (102 days lag). Lastly, the outbreak prediction accuracy of the model using dynamically updated $$R_t$$ R t produced an average AUROC of 0.72 (0.708, 0.723) compared to 0.56 (0.555, 0.568) when $$R_t$$ R t was kept constant. Monitoring the evolution of $$R_t$$ R t during an epidemic is an important complementary piece of information to reported daily counts, recoveries and deaths, since it provides an early signal of the efficacy of containment measures. Using updated $$R_t$$ R t values produces significantly better predictions of future outbreaks. Our results found variation in the effect of early public health interventions on the evolution of $$R_t$$ R t over time and across countries, which could not be explained solely by the timing and number of the adopted interventions.

scholarly journals Speculative housing markets and rent control: insights from nonlinear economic dynamics

2021 ◽  
Author(s):  
Noemi Schmitt ◽  
Frank Westerhoff
Keyword(s):  
Housing Market ◽  
Housing Stock ◽  
House Price ◽  
Housing Markets ◽  
Market Model ◽  
Market Dynamics ◽  
Rent Control ◽  
Forecasting Accuracy ◽  
Expectation Formation ◽  
Rent Level

AbstractWe propose a novel housing market model to explore the effectiveness of rent control. Our model reveals that the expectation formation and learning behavior of boundedly rational homebuyers, switching between extrapolative and regressive expectation rules subject to their past forecasting accuracy, may create endogenous housing market dynamics. We show that policymakers may use rent control to reduce the rent level, although such policies may have undesirable effects on the house price and the housing stock. However, we are also able to prove that well-designed rent control may help policymakers to stabilize housing market dynamics, even without creating housing market distortions.

scholarly journals Nowcasting Regional GDP: The Case of the Free State of Saxony

2015 ◽  
Vol 66 (1) ◽  
pp. 71-98
Author(s):  
Steffen R. Henzel ◽  
Robert Lehmann ◽  
Klaus Wohlrabe
Keyword(s):  
Data Structure ◽  
Manufacturing Sector ◽  
Regional Level ◽  
Free State ◽  
Large Set ◽  
Regional Survey ◽  
Forecasting Accuracy ◽  
Leading Indicator ◽  
Information Sets ◽  
Survey Results

Abstract We tackle the nowcasting problem at the regional level, using a large set of indicators (regional, national and international) for the years 1998 to 2013. We explicitly take into account the ragged-edge data structure and consider the different information sets faced by a regional forecaster within each quarter. It appears that regional survey results in particular improve forecasting accuracy. Among the 10% best performing models for the short forecasting horizon, one fourth contain regional indicators. Hard indicators from the German manufacturing sector and the Composite Leading Indicator for Europe also deliver useful information for the prediction of regional GDP in Saxony. Unlike national GDP forecasts, the performance of regional GDP is similar across different information sets within a quarter.

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