A Comparative Study on Fashion Demand Forecasting Models with Multiple Sources of Uncertainty

Ren, Simiao; Chan, Hau‐Ling; Ram, Pratibha

doi:10.1007/s10479-016-2204-6

Cited by 38 publications

(17 citation statements)

References 57 publications

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“…Economic forecasting requires the inclusion of the study of trendy, seasonal, cyclical, and random aspects [28,29]. Influence of consumer demand of similar structure on the results of organization activity was investigated in [30,31].…”

Section: Seasonal Fluctuationsmentioning

confidence: 99%

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Considering Random Factors in Modeling Complex Microeconomic Systems

et al. 2020

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Within the framework of a model describing real-functioning association of three enterprises, numerical calculations of economic dynamics parameters considering fluctuating market demand for the goods were performed. A methodology was suggested for approximated consideration of both seasonal and random demand fluctuations at the market of textile garments; the main steps of the suggested methodology were described. The main exogenous random factors within this model include, as stated above, the volume of market demand for the goods produced by the enterprises of the group. The basic volume of market demand is considered at the average actual level according to the results of the enterprises’ analysis, and additionally we take into account the influence of non-price factors, such as random changes in the consumers’ tastes, consumers’ income, and other random factors on the market demand. By volume of market demand, we consider the total amount of goods produced by the enterprises of the group that all consumers are willing and able to purchase at a specific price in a marketplace. The calculations were made based on actual values of external economic parameters, such as labor cost, product prices, etc. Influence of the market demand fluctuations on the companies’ activity has been illustrated both numerically and graphically, allowing the analysis of the impact of exogenous parameters on the companies output and profits. The suggested approach creates a basis for further analysis of the impact of random factors of a similar nature, i.e., stochastic shocks related to the level of interest rates, shifts and turnabouts in the social environment, as well as the market transformations due to annual/seasonal epidemics.

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Section: Seasonal Fluctuationsmentioning

confidence: 99%

“…Influence of consumer demand of similar structure on the results of organization activity was investigated in [30,31]. Production and sales output level forecasting has been studied in [29][30][31][32].…”

Section: Seasonal Fluctuationsmentioning

confidence: 99%

Considering Random Factors in Modeling Complex Microeconomic Systems

et al. 2020

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“…The addition of power coefficient has made the traditional model become a nonlinear model. In recent years, high accuracy has enabled it to be widely applied in numerous fields for prediction [34,35].…”

Section: Ngm Prediction Modelmentioning

confidence: 99%

Prediction of the Energy Consumption Variation Trend in South Africa based on ARIMA, NGM and NGM-ARIMA Models

Wang

2019

Energies

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South Africa’s energy consumption takes up about one-third of that in the whole African continent, ranking the first place in Africa. However, there are few researches on the prediction of energy consumption in South Africa. In this study, based on the data of South Africa’s energy consumption during 1998–2016, Autoregressive Integrated Moving Average (ARIMA) model, nonlinear grey model (NGM) and nonlinear grey model–autoregressive integrated moving average (NGM-ARIMA) model are adopted to predict South Africa’s energy consumption during 2017–2030. After using these NGM, ARIMA and NGM-ARIMA, the mean absolute percent errors (MAPE) are 2.827%, 2.655% and 1.772%, respectively, which indicates that the predicted result has very high reliability. The prediction results show that the energy consumption in South Africa will keep increasing with the growth rate of about 7.49% in the next 14 years. This research result will provide scientific basis for the policy adjustment of energy supply and demand in South Africa and the prediction techniques used in the research will have reference function for the energy consumption study in other African countries.

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“…The traditional insurance marketing model, lacking complete information on sales and consumer feedbacks and thorough analysis on market demand, faces problems like unclear market orientation, limited target consumers, and unrealistic business mode [1][2][3][4][5]. These problems can be overcome by analyzing the historical data on insurance sales mathematically, and predicting the sales in future.…”

Section: Introductionmentioning

confidence: 99%

An Insurance Sales Prediction Model Based on Deep Learning

Wang¹

2020

RIA

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The analysis and prediction of insurance sales enable insurance companies to construct effective and efficient marketing models, conduct targeted promotion campaigns, and expand the range of target consumers, thereby increasing the direct and indirect profits. This paper improves the long short-term memory (LSTM) network into an effective prediction model for insurance sales. Firstly, an original database was created on the historical data of insurance sales, followed by the identification of the linear and nonlinear factors affecting insurance sales. Next, the redundant data were removed from data sequences through nondimensionalization and gray correlation calculation. Finally, multiple linear regression (MLR) was carried out based on LINEST function, completing the prediction of insurance sales. Simulation results show that the improved LSTM network outperformed the other prediction models, indicating that the prediction effect can be improved by data processing through multiple residual predictions.

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A Comparative Study on Fashion Demand Forecasting Models with Multiple Sources of Uncertainty

Cited by 38 publications

References 57 publications

Considering Random Factors in Modeling Complex Microeconomic Systems

Considering Random Factors in Modeling Complex Microeconomic Systems

Prediction of the Energy Consumption Variation Trend in South Africa based on ARIMA, NGM and NGM-ARIMA Models

An Insurance Sales Prediction Model Based on Deep Learning

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