Predicting product demand from small-sized data: grey models

Özdemir, Aslı; Özdağoğlu, Güzin

doi:10.1108/gs-10-2016-0038

Cited by 20 publications

(9 citation statements)

References 27 publications

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“…Sistem Grey ini kemudian dikembangkan menjadi suatu metode peramalan Grey sebagai pengganti dari model deret waktu dan metode regresi dengan menggunakan model diferensial pada teknik penyelesaiannya. Model Grey telah banyak diaplikasikan pada penelitian-penelitian berbasis peramalan, seperti model predikasi volume lalu lintas [5], prediksi kecelakaan kebakaran [6], serta prediksi permintaan produk [7]. Model Grey ini secara umum dikenal dengan nama model Grey ( , ), dimana menunjukkan berapa kali diferensial dilakukan dan menunjukkan jumlah variabel yang digunakan.…”

Section: Pendahuluanunclassified

Model Grey (1,1) dan Grey-Markov pada Peramalan Realisasi Penerimaan Negara

Ahdika

2018

JFOURIER

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Setiap Negara memerlukan sumber penerimaan untuk mewujudkan pembangunan nasional dan membiayai segala keperluannya. Di Indonesia, terdapat tiga sumber utama penerimaan Negara; penerimaan pajak, penerimaan bukan pajak, serta penerimaan hibah baik dari dalam maupun luar negeri. Untuk mengantisipasi berbagai keperluan serta mengoptimalkan penggunaan penerimaan Negara, maka perlu adanya proyeksi realisasi penerimaan Negara dari ketiga sumber tersebut. Proyeksi tersebut dapat dilakukan dengan menggunakan model deret waktu klasik baik model deterministik maupun model stokastik. Namun demikian, pada model deret waktu klasik terdapat beberapa asumsi yang harus dipenuhi seperti pola data atau jumlah minimal data. Sebagai alternatif, pada penelitian ini akan dilakukan peramalan dengan menggunakan model Grey (1,1) dan Grey-Markov; perpaduan antara model Grey dengan analisis Rantai Markov. Model ini memiliki keunggulan dibandingkan model deret waktu klasik, yaitu tidak perlu adanya asumsi mengenai pola data serta peramalan dapat dilakukan meskipun data yang dimiliki cukup kecil (minimal 4 data). Hasil analisis menunjukkan bahwa model Grey-Markov secara umum memberikan akurasi peramalan yang lebih baik dibandingkan dengan model Grey (1,1). [Every state requires a source of revenue to realize its national development and fund its needs. In Indonesia, there are three main sources of state revenues; tax, non-tax, and grant revenues both from within and outside the country. To anticipate various purposes and optimize the use of state revenues, it is necessary to project the realization of the revenues from these three sources. The projection can be done using a classical time series model in both deterministic and stochastic models. However, in the classical time series model there are several assumptions that must be met such as data patterns or minimal amount of data. Alternatively, in this study the data will be forecasted using Grey (1,1) model and Grey-Markov model; a combination of the Grey model with Markov Chain analysis. This models have advantages over the classical time series models, ie no need for assumption about data pattern and forecasting can be done even though there are small size data (at least 4 data). The results of the analysis show that generally Grey-Markov model provides better forecasting accuracy compared with Grey (1,1) model.]

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Section: Pendahuluanunclassified

Model Grey (1,1) dan Grey-Markov pada Peramalan Realisasi Penerimaan Negara

Ahdika

2018

JFOURIER

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“…Grey systems theory in a decision-making process is very useful to tackle the disadvantage of AHP. Recently, grey theory has been used for a variety of applications (Abdulshahed et al, 2017;Eshtaiwi et al, 2017;Özdemir and Özdagoglu, 2017;Zeng and Luo, 2017;Ayvaz et al, 2017;Wang et al, 2017).…”

Section: Grey-based Assessment Approachmentioning

confidence: 99%

A grey-based assessment approach to the site selection of a desalination plant in Libya

Badi

Abdulshahed

Shetwan

et al. 2018

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Purpose The purpose of this paper is to propose a site selection method using grey system theory for a desalination plant in Libya. Design/methodology/approach In order to tackle incompleteness and imprecision of human’s judgments, grey numbers were used. This work uses a grey-based approach to represent decision makers’ comparison judgments and extent analysis method to select the best site. Therefore, a real case study of a selection problem of a site selection of desalination plant in Libya was used to illustrate the proposed approach. Findings Site selection in a desalination plant can be one of the most important decisions in planning a desalination project. The decision affects both the project cost and potentially the project schedule. Based on the results of grey model, a clear order of these sites and the degree of preference are obtained. This paper presents a way to improve a site selection by using a grey model, especially in a complex environment like Libya. Originality/value To the best knowledge of the authors, there is no literature for site selection using grey system theory in a desalination plant in Libya. This attempt may well enhance and facilitate the decision-making process of the best site in the country involved in this research.

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“…Aydemir developed an EPQ model by degree of greyness approach [23]. Özdemir applied grey model to predict the product demand [24]. Ma studied the framework of grey machine learning [25].…”

Section: Introductionmentioning

confidence: 99%

A Novel Fractional-Order Grey Prediction Model and Its Modeling Error Analysis

Meng

Zeng

2019

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Based on the grey prediction model GM(1,1), a novel fractional-order grey prediction model is proposed and its modeling error is systematically studied. In this paper, exponential data sequences are generated for numerical simulation. Via the numerical simulation method, the mean absolute percentage error (MAPE) of the fractional-order GM(1,1) with different values of order and development coefficient is compared to the GM(1,1) and the discrete GM(1,1). The error distribution of the sequences of exponential data is given. The GM(1,1) and the direct modeling GM(1,1) are both special cases of the fractional-order GM(1,1). The conclusion is helpful to further optimize the grey model using fractional-order operators and to expand the applicable bound of GM(1,1).

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Predicting product demand from small-sized data: grey models

Cited by 20 publications

References 27 publications

Model Grey (1,1) dan Grey-Markov pada Peramalan Realisasi Penerimaan Negara

Model Grey (1,1) dan Grey-Markov pada Peramalan Realisasi Penerimaan Negara

A grey-based assessment approach to the site selection of a desalination plant in Libya

A Novel Fractional-Order Grey Prediction Model and Its Modeling Error Analysis

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