Relative performance of methods for forecasting special events

Νικολόπουλος, Κωνσταντίνος; Litsa, Akrivi; Petropoulos, Fotios; Bougioukos, Vasileios; Khammash, Marwan

doi:10.1016/j.jbusres.2015.03.037

Cited by 31 publications

(19 citation statements)

References 31 publications

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“…In Unaided Judgement, individuals are not provided with any form of guidance about forecasting; this is the standard benchmark of judgemental forecasting (Green & Armstrong, 2007). However, it is not without flaws, which has prompted academic researchers to suggest using structured judgemental forecasting methods or tools to predict the outcome of projects over less structured methods (Armstrong, 1986;2001;Nikolopoulos et al 2015). In principle, although unaided judgement can provide useful information, this method of forecasting produces inaccurate forecasts as the forecasters may not always be able to recall analogous cases correctly (Green, 2002;Lee, Goodwin, Fildes, Nikolopoulos & Lawrence, 2007).…”

Section: Forecasting Megaprojectsmentioning

confidence: 99%

Relative performance of judgmental methods for forecasting the success of megaprojects

Litsiou

Polychronakis

Karami

et al. 2022

International Journal of Forecasting

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Section: Forecasting Megaprojectsmentioning

confidence: 99%

Relative performance of judgmental methods for forecasting the success of megaprojects

Litsiou

Polychronakis

Karami

et al. 2022

International Journal of Forecasting

Self Cite

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“…industri ritel yang dapat membuat permintaan berubah-ubah. Dampak promosi terhadap dinamika permintaan telah sering diteliti secara luas [7,8,9,10,11,12]. Perubahan permintaan adalah risiko yang menantang rantai pasokan produk dan menjadi perhatian manajer dan praktisi [13].…”

Section: Informal | 122 Issn : 2503 -250xunclassified

Penerapan Metode Peramalan untuk Identifikasi Permintaan Konsumen

Auliasari¹,

Kertaningtyas²,

Kriswantono³

2020

INFORMAL

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The forecast model is done using data from several years before, with the involvement of time parameters in the forecast process is usable for the company to made an effective and efficient planning. Forecasting has an important role because the company requires short-term, medium-term and long-term estimates for each management. For short-term estimates, a company requires personnel, production and transportation scheduling, which is part of the process of scheduling and estimating consumer demand. In this study the results of three forecasting methods were compared, there is simple average, naïve and seasonal naive on demand data of PT SUPER SUKSES NIAGA to be further these three method measured its forecast accuracy using the value of MASE (Mean Absolute Square Error). From the results of data pre-processing consumers whose high value of demand are PT. DIESELINDO, PT. DUTA, PT. HEXINDO and PT. PANATAMA. The results of forecasting shown that the method that has the smallest MASE value is the simple moving average method.

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“…Machine learning is a branch of computer science in which data could teach algorithms. The learning process could be done as supervised-, unsupervised, and/or semi-supervised learning forms (Mitchell 1997, Arkes 2001, Armstrong 2001, Nikolopoulos, Litsa et al 2015, Maleki, Mahmoudi et al 2020. In this section, some approaches that are used for prediction of cases (confirmed and deaths) of…”

Section: Predictionmentioning

confidence: 99%

Analysis and Prediction of COVID-19 using SIR, SEIR, and Machine Learning Models: Australia, Italy, and UK Cases

Rahimi

Gandomi

Chen

2020

Preprint

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The novel Coronavirus disease, known as COVID-19, is an outbreak that started in Wuhan, one of the Central Chinese cities. In this report, a short analysis focusing on Australia, Italy, and the United Kingdom has been conducted. The analysis includes confirmed and recovered cases and deaths, the growth rate in Australia as compared with Italy and the United Kingdom, and the outbreak in different Australian cities. Mathematical approaches based on the susceptible, infected, and recovered case (SIR) and susceptible, exposed, infected, and recovered (SEIR) models were proposed to predict the epidemiology in the countries. Since the performance of the classic form of SIR and SEIR depends on parameter settings, some optimization algorithms, namely, the Broyden–Fletcher–Goldfarb–Shanno (BFGS), conjugate gradients (CG), L-BFGS-B, and Nelder-Mead are proposed to optimize the parameters of SIR and SEIR models and improve its predictive capabilities. The results of optimized SIR and SEIR models are compared with the Prophet algorithm and logistic function as two known ML algorithms. The results show that different algorithms display different behaviours in different countries. However, the improved version of the SIR and SEIR models have a better performance compared with other mentioned algorithms described in this study. Moreover, the Prophet algorithm works better for Italy and the United Kingdom cases than for Australian cases and Logistic function compared with Prophet algorithm has a better performance in these cases. It seems that Prophet algorithm is suitable for data with increasing trend in pandemic situations. Optimization of the SIR and SEIR models parameters has yielded a significant improvement in the prediction accuracy of the models. Although there are several algorithms for prediction of this Pandemic, there is no certain algorithm that would be the best one for all cases.

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Relative performance of methods for forecasting special events

Cited by 31 publications

References 31 publications

Relative performance of judgmental methods for forecasting the success of megaprojects

Relative performance of judgmental methods for forecasting the success of megaprojects

Penerapan Metode Peramalan untuk Identifikasi Permintaan Konsumen

Analysis and Prediction of COVID-19 using SIR, SEIR, and Machine Learning Models: Australia, Italy, and UK Cases

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