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Article
Publication date: 29 April 2021

Huan Wang, Yuhong Wang and Dongdong Wu

To predict the passenger volume reasonably and accurately, this paper fills the gap in the research of quarterly data forecast of railway passenger volume. The research…

Abstract

Purpose

To predict the passenger volume reasonably and accurately, this paper fills the gap in the research of quarterly data forecast of railway passenger volume. The research results can also provide references for railway departments to plan railway operation lines reasonably and efficiently.

Design/methodology/approach

This paper intends to establish a seasonal cycle first order univariate grey model (GM(1,1) model) combing with a seasonal index. GM (1,1) is termed as the trend equation to fit the railway passenger volume in China from 2014 to 2018. The railway passenger volume in 2019 is used as the experimental data to verify the forecasting effect of the proposed model. The forecasting results of the seasonal cycle GM (1,1) model are compared with the traditional GM (1,1) model, seasonal grey model (SGM(1,1)), Seasonal Autoregressive Integrated Moving Average (SARIMA) model, moving average method and exponential smoothing method. Finally, the authors forecast the railway passenger volume from 2020 to 2022.

Findings

The quarterly data of national railway passenger volume have a clear tendency of cyclical fluctuations and show an annual growth trend. According to the comparison of the modeling results, the authors know that the seasonal cycle GM (1,1) model has the best prediction effect with the mean absolute percentage error of 1.32%. It is much better than the other models, reflecting the feasibility of the proposed model.

Originality/value

As the previous grey prediction model could not solve the series prediction problem with seasonal fluctuation, and there are few research studies on quarterly railway passenger volume forecasting, GM (1,1) model is taken as the trend equation and combined with the seasonal index to construct a combination forecasting model for accurate forecasting results in this study. Besides, considering the impact of the epidemic on passenger volume, the authors introduce a disturbance factor to deal with the forecasting results in 2020, making the modeling results more scientific, practical and referential.

Details

Grey Systems: Theory and Application, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2043-9377

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Article
Publication date: 11 February 2021

Xiaoyue Zhu, Yaoguo Dang and Song Ding

Aiming to address the forecasting dilemma of seasonal air quality, the authors design the novel self-adaptive seasonal adjustment factor to extract the seasonal

Abstract

Purpose

Aiming to address the forecasting dilemma of seasonal air quality, the authors design the novel self-adaptive seasonal adjustment factor to extract the seasonal fluctuation information about the air quality index. Based on the novel self-adaptive seasonal adjustment factor, the novel seasonal grey forecasting models are established to predict the air quality in China.

Design/methodology/approach

This paper constructs a novel self-adaptive seasonal adjustment factor for quantifying the seasonal difference information of air quality. The novel self-adaptive seasonal adjustment factor reflects the periodic fluctuations of air quality. Therefore, it is employed to optimize the data generation of three conventional grey models, consisting of the GM(1,1) model, the discrete grey model and the fractional-order grey model. Then three novel self-adaptive seasonal grey forecasting models, including the self-adaptive seasonal GM(1,1) model (SAGM(1,1)), the self-adaptive seasonal discrete grey model (SADGM(1,1)) and the self-adaptive seasonal fractional-order grey model (SAFGM(1,1)), are put forward for prognosticating the air quality of all provinces in China .

Findings

The experiment results confirm that the novel self-adaptive seasonal adjustment factors promote the precision of the conventional grey models remarkably. Simultaneously, compared with three non-seasonal grey forecasting models and the SARIMA model, the performance of self-adaptive seasonal grey forecasting models is outstanding, which indicates that they capture the seasonal changes of air quality more efficiently.

Research limitations/implications

Since air quality is affected by various factors, subsequent research may consider including meteorological conditions, pollutant emissions and other factors to perfect the self-adaptive seasonal grey models.

Practical implications

Given the problematic air pollution situation in China, timely and accurate air quality forecasting technology is exceptionally crucial for mitigating their adverse effects on the environment and human health. The paper proposes three self-adaptive seasonal grey forecasting models to forecast the air quality index of all provinces in China, which improves the adaptability of conventional grey models and provides more efficient prediction tools for air quality.

Originality/value

The self-adaptive seasonal adjustment factors are constructed to characterize the seasonal fluctuations of air quality index. Three novel self-adaptive seasonal grey forecasting models are established for prognosticating the air quality of all provinces in China. The robustness of the proposed grey models is reinforced by integrating the seasonal irregularity. The proposed methods acquire better forecasting precisions compared with the non-seasonal grey models and the SARIMA model.

Details

Grey Systems: Theory and Application, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2043-9377

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Book part
Publication date: 17 November 2010

John F. Kros and Christopher M. Keller

This chapter presents an Excel-based regression analysis to forecast seasonal demand for U.S. Imported Beer sales data. The following seasonal regression models are…

Abstract

This chapter presents an Excel-based regression analysis to forecast seasonal demand for U.S. Imported Beer sales data. The following seasonal regression models are presented and interpreted including a simple yearly model, a quarterly model, a semi-annual model, and a monthly model. The results of the models are compared and a discussion of each model's efficacy is provided. The yearly model does the best at forecasting U.S. Import Beer sales. However, the yearly does not provide a window into shorter-term (i.e., monthly) forecasting periods and subsequent peaks and valleys in demand. Although the monthly seasonal regression model does not explain as much variance in the data as the yearly model it fits the actual data very well. The monthly model is considered a good forecasting model based on the significance of the regression statistics and low mean absolute percentage error. Therefore, it can be concluded that the monthly seasonal model presented is doing an overall good job of forecasting U.S. Import Beer Sales and assisting managers in shorter time frame forecasting.

Details

Advances in Business and Management Forecasting
Type: Book
ISBN: 978-0-85724-201-3

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Book part
Publication date: 12 November 2014

John F. Kros, W. Jason Rowe and Evelyn C. Brown

Demand seasonality in the U.S. Imported Beer industry is common. The financial cycles of the past decade brought some extreme fluctuations to industry demand, which was…

Abstract

Demand seasonality in the U.S. Imported Beer industry is common. The financial cycles of the past decade brought some extreme fluctuations to industry demand, which was trending upward. This research extends previous work in this area by comparing seasonal forecasting models for two time periods: 1999–2007 and 1999–2012. The previous study (Kros & Keller, 2010) examined the 1999–2007 time frame while this study extends their model using the new data. Models are developed within Excel and include a simple yearly model, a semi-annual model, a quarterly model, and a monthly model. The results of the models are compared and a discussion of each model’s efficacy is provided. While, the models did do a good job forecasting U.S. Import Beer sales from 1999 to 2007 the economic downturn starting in 2007 was deleterious to some models continued efficacy. When the data from the downturn is accounted for it is concluded that the seasonal models presented are doing an overall good job of forecasting U.S. Import Beer Sales and assisting managers in shorter time frame forecasting.

Details

Advances in Business and Management Forecasting
Type: Book
ISBN: 978-1-78441-209-8

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Article
Publication date: 31 October 2018

Chung-Han Ho, Ping-Teng Chang, Kuo-Chen Hung and Kuo-Ping Lin

The purpose of this paper is to develop a novel intuitionistic fuzzy seasonality regression (IFSR) with particle swarm optimization (PSO) algorithms to accurately forecast

Abstract

Purpose

The purpose of this paper is to develop a novel intuitionistic fuzzy seasonality regression (IFSR) with particle swarm optimization (PSO) algorithms to accurately forecast air pollutions, which are typical seasonal time series data. Seasonal time series prediction is a critical topic, and some time series data contain uncertain or unpredictable factors. To handle such seasonal factors and uncertain forecasting seasonal time series data, the proposed IFSR with the PSO method effectively extends the intuitionistic fuzzy linear regression (IFLR).

Design/methodology/approach

The prediction model sets up IFLR with spreads unrestricted so as to correctly approach the trend of seasonal time series data when the decomposition method is used. PSO algorithms were simultaneously employed to select the parameters of the IFSR model. In this study, IFSR with the PSO method was first compared with fuzzy seasonality regression, providing evidence that the concept of the intuitionistic fuzzy set can improve performance in forecasting the daily concentration of carbon monoxide (CO). Furthermore, the risk management system also implemented is based on the forecasting results for decision-maker.

Findings

Seasonal autoregressive integrated moving average and deep belief network were then employed as comparative models for forecasting the daily concentration of CO. The empirical results of the proposed IFSR with PSO model revealed improved performance regarding forecasting accuracy, compared with the other methods.

Originality/value

This study presents IFSR with PSO to accurately forecast air pollutions. The proposed IFSR with PSO model can efficiently provide credible values of prediction for seasonal time series data in uncertain environments.

Details

Industrial Management & Data Systems, vol. 119 no. 3
Type: Research Article
ISSN: 0263-5577

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Article
Publication date: 23 November 2012

Faruk Balli and Elsayed Mousa Elsamadisy

This paper seeks to model the daily and weekly forecasting of the currency in circulation (CIC) for the State of Qatar.

Abstract

Purpose

This paper seeks to model the daily and weekly forecasting of the currency in circulation (CIC) for the State of Qatar.

Design/methodology/approach

The paper employs linear forecasting models, the regression model and the seasonal ARIMA model to forecast the CIC for Qatar.

Findings

Comparing the linear methods, the seasonal ARIMA model provides better estimates for short‐term forecasts. The range of forecast errors for the seasonal ARIMA model forecasts are less than 100 million QR for the short‐term CIC forecasts.

Practical implications

The findings of this paper suggest that the CIC in Qatar is in a pattern and it would be easier to forecast the currency in circulation in Qatar economy. Accurate estimates of money market liquidity would help Qatar Central bank, to maintain the price stability in the Qatar economy.

Originality/value

This paper forecasts the currency in circulation for the State of Qatar. Additionally, the empirical part of the paper compares the different methodologies find the appropriate model for the CIC for the state of Qatar.

Details

International Journal of Islamic and Middle Eastern Finance and Management, vol. 5 no. 4
Type: Research Article
ISSN: 1753-8394

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Article
Publication date: 1 April 2001

Albert Caruana

Forecasting enables the efficient utilisation of a firm’s resources. There are various types of forecasting models that can be built. Illustrates the steps involved in…

Abstract

Forecasting enables the efficient utilisation of a firm’s resources. There are various types of forecasting models that can be built. Illustrates the steps involved in building a forecasting model utilising seasonal regression with a practical example. The model obtained for the carbonated soft drink brand under consideration estimates a growth rate of 3,568 units per month during the last five years and identifies the seasonal effect during each month of the year. The model also computes the cannibalisation effect that the introduction of a brand extension has had. The development of such models can provide a useful input to both marketing and operations planning.

Details

Journal of Product & Brand Management, vol. 10 no. 2
Type: Research Article
ISSN: 1061-0421

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Article
Publication date: 1 January 1989

Peter W. Stonebraker and Pricha Pantumsinchai

The effects of trading day adjustment techniques to improveforecasting accuracy are investigated. The results of forecasting fromdirectly generated monthly data are…

Abstract

The effects of trading day adjustment techniques to improve forecasting accuracy are investigated. The results of forecasting from directly generated monthly data are compared with those of transformed (trading day adjusted) data. Conclusions are offered towards the development of a trading day adjustment model.

Details

International Journal of Operations & Production Management, vol. 9 no. 1
Type: Research Article
ISSN: 0144-3577

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Article
Publication date: 1 March 1990

Shaw K. Chen, William J. Wrobleski and David J. Brophy

This paper examines the empirical patterns of futures prices volatility by using different seasonal adjustment techniques The average absolute month to month percentage…

Abstract

This paper examines the empirical patterns of futures prices volatility by using different seasonal adjustment techniques The average absolute month to month percentage (AAPC) figures are used to describe the extent of smoothness when seasonal adjustment methods are applied. Several interesting patterns are suggested from the observation of different futures contracts. The authors then suggest further that if seasonal patterns do exist for futures prices volatility, it is possible to focus the study of futures prices volatility on the different seasonal filters selection, and/or on the different seasonal models alternatives.

Details

Managerial Finance, vol. 16 no. 3
Type: Research Article
ISSN: 0307-4358

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Article
Publication date: 27 March 2020

Luyao Wang, Jianying Feng, Xiaojie Sui, Xiaoquan Chu and Weisong Mu

The purpose of this paper is to provide reference for researchers by reviewing the research advances and trend of agricultural product price forecasting methods in recent years.

Abstract

Purpose

The purpose of this paper is to provide reference for researchers by reviewing the research advances and trend of agricultural product price forecasting methods in recent years.

Design/methodology/approach

This paper reviews the main research methods and their application of forecasting of agricultural product prices, summarizes the application examples of common forecasting methods, and prospects the future research directions.

Findings

1) It is the trend to use hybrid models to predict agricultural products prices in the future research; 2) the application of the prediction model based on price influencing factors should be further expanded in the future research; 3) the performance of the model should be evaluated based on DS rather than just error-based metrics in the future research; 4) seasonal adjustment models can be applied to the difficult seasonal forecasting tasks in the agriculture product prices in the future research; 5) hybrid optimization algorithm can be used to improve the prediction performance of the model in the future research.

Originality/value

The methods from this paper can provide reference for researchers, and the research trends proposed at the end of this paper can provide solutions or new research directions for relevant researchers.

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