Population Spatial Migration Tendency Forecasting with Grey Model and Fourier Series

Jiang, Chang; Wang, Jun; Yang, Le; Shang, Ji Bin; Shi, Yun

doi:10.4028/www.scientific.net/amm.409-410.69

Cited by 3 publications

(4 citation statements)

References 6 publications

(7 reference statements)

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“…Their model also represented high reliability and suitability for practical use. Jiang et al (2013) used the Fourier series model to improve the performance using the Gray model to know population spatial migration tendency more accurately. The combination of these two models produced satisfactory results.…”

Section: Literature Reviewmentioning

confidence: 99%

Forecast of sales of selected food products in retail using Fourier series analysis and non-linear regression

Kačmáry,

Bindzár,

Kovalčík

et al. 2024

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Purpose The purpose of this paper is to apply and verify Fourier series analysis in combination with non-linear regression as a tool of forecasting and planning of inputs in the logistics process of a retail chain store. Design/methodology/approach For many popular products, a significant effect of seasonality of sales is expected; therefore, the method of Fourier series was chosen as one of the main forecast calculation techniques. However, the use of this method directly for forecasting sales has a limitation in the form of a complete reconstruction of the shape of the curve from of the given monitored time. Thus, the forecast is based only on the significant harmonic components from the Fourier series analysis that will participate in forecast forming. In addition, to respect the trend of series, the results of Fourier series analysis are combined with the non-linear regression. Findings The results showed that the number of significant harmonic components from the Fourier series analysis is suitable to reflect the future behaviour of the sale in standard market conditions. Forecasting of the sale and accurate purchase planning of goods has a positive effect on reducing the waste of unsold products after their shelf and on increasing of a customer satisfaction. Research limitations/implications This study has an application in a certain period of time (relatively calm behaviour of the food market) and only for a certain region. Therefore, it is not possible to generalize these results as the behaviour of consumers, e.g. within the state. It will also be interesting to monitor and forecast sales of other food items. Practical implications This provides a practical and relatively simple tool for implementing or improving the process of forecasting seasonally dependent products in the food industry. Originality/value This study shows the possibility of forecast that is based on adding the significant harmonic components from the Fourier series analysis to form forecast with the non-linear regression.

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Section: Literature Reviewmentioning

confidence: 99%

Forecast of sales of selected food products in retail using Fourier series analysis and non-linear regression

Kačmáry,

Bindzár,

Kovalčík

et al. 2024

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“…Tourist flow data is typical time series data. Existing time series data prediction methods mainly include the following: (a) gray model method (Jiang et al, 2013; Xiao & Duan, 2020); (b) traditional time series modeling methods, such as the autoregressive integrated moving average (ARIMA) model (Calheiros, Masoumi, Ranjan, & Buyya, 2015; Liu, Tian, & Li, 2015; Shukur & Lee, 2015) and random forests (Creamer, 2011; Kusiak, Verma, & Wei, 2013); (c) time series decomposition methods, including spectrum analysis, time series analysis, and Fourier series analysis (Hassani, Soofi, & Zhigljavsky, 2010; Jiang et al, 2013; Tsai, Chen, & Chang, 2016); (d) neural network models, such as the back propagation neural network (BPNN) model (Yang, Li, & Wu, 2017; Zhang, Cui, Feng, Gong, & Hu, 2019) and the recurrent neural network model (Selvin, Vinayakumar, Gopalakrishnan, Menon, & Soman, 2017; Tian & Pan, 2015). The above research methods have great research value for the prediction of tourist flow in scenic spots, but they still have some shortcomings.…”

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

“…(c) time series decomposition methods, including spectrum analysis, time series analysis, and Fourier series analysis (Hassani, Soofi, & Zhigljavsky, 2010;Jiang et al, 2013;Tsai, Chen, & Chang, 2016); (d) neural network models, such as the back propagation neural network (BPNN) model (Yang, Li, & Wu, 2017;Zhang, Cui, Feng, Gong, & Hu, 2019) and the recurrent neural network model (Selvin, Vinayakumar, Gopalakrishnan, Menon, & Soman, 2017;Tian & Pan, 2015). The above research methods have great research value for the prediction of tourist flow in scenic spots, but they still have some shortcomings.…”

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confidence: 99%

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Prediction of tourist flow based on multi‐source traffic data in scenic spot

Zhang

et al. 2020

Transactions in GIS

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At present, China's tourism industry has entered an era of rapid development. It is of great significance to predict the precise and real‐time tourist flow of the popular tourist areas with dense crowds, providing decision support for rational evacuation of tourist flow, activation of emergency plans, and prevention of security accidents. This article presents a long short‐term memory (LSTM) neural network model based on the global attention mechanism. The model first uses two LSTM layers to calculate attention weights using multi‐source data at different time steps to improve the model’s learning ability. Then the model predicts the tourist flow of the scenic spots in the next time period based on the weights and outputs previously obtained. At the final stage, the pre‐trained parameters of the network are used to initialize the model. To verify the validity of the model, we compared it with the LSTM model, back propagation neural network model, and autoregressive integrated moving average model based on the data of Beijing’s South Luogu Lane scenic spot. It turned out that the results of our solution were more in line with the true value, which proves that it is feasible for real‐time prediction of tourist flow in popular scenic spots.

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Application of Fourier grey model (FGM) for demand forecasting and Markov chain method for inventory planning

Puspitasari,

Saraswati,

Shabrina

2023

The 2ND NATIONAL CONFERENCE ON MATHEMATICS EDUCATION (NACOME) 2021: Mathematical Proof as a Tool for Learning Mathematics

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Population Spatial Migration Tendency Forecasting with Grey Model and Fourier Series

Cited by 3 publications

References 6 publications

Forecast of sales of selected food products in retail using Fourier series analysis and non-linear regression

Forecast of sales of selected food products in retail using Fourier series analysis and non-linear regression

Prediction of tourist flow based on multi‐source traffic data in scenic spot

Application of Fourier grey model (FGM) for demand forecasting and Markov chain method for inventory planning

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