On the empirical performance of some new neural network methods for forecasting intermittent demand

2020 ◽  
Vol 31 (3) ◽  
pp. 281-305
Author(s):  
M Z Babai ◽  
A Tsadiras ◽  
C Papadopoulos
Keyword(s):  
Neural Network ◽  
Empirical Investigation ◽  
Forecast Accuracy ◽  
Spare Parts ◽  
Intermittent Demand ◽  
Network Methods ◽  
Empirical Performance ◽  
Single Exponential ◽  
Better Than ◽  
Airline Company

Abstract In this paper, new neural network (NN) methods are proposed to forecast intermittent demand and we empirically study their performance as compared to parametric and non-parametric forecasting methods proposed in the literature. The empirical investigation uses demand data for 5,135 spare parts for the fleet of aircrafts of an airline company. Three parametric benchmark methods are examined: single exponential smoothing (SES), Croston’s method and Syntetos–Boylan approximation, along with two bootstrapping methods: Willemain’s method and Zhou and Viswanathan’s method. The benchmark NN method considered in this paper is that proposed by Gutierrez et al. (2008) The paper shows the outperformance of SES and the NN methods for (a) their forecast accuracy and (b) their inventory efficiency (trade-off between holding volumes and backordering volumes) when compared to the other methods. Moreover, among the NN methods, a new proposed method is shown to be better than that proposed by Gutierrez et al. in terms of forecast accuracy and inventory efficiency.

scholarly journals Intermittent demand forecasting for aircraft inventories: a study of Brazilian’s Boeing 737NG aircraft´s spare parts management

TRANSPORTES ◽  
2019 ◽  
Vol 27 (2) ◽  
pp. 102-116
Author(s):  
Jersone Tasso Moreira Silva ◽  
Luiz Henrique Santos ◽  
Alexandre Teixeira Dias ◽  
Hugo Ferreira Braga Tadeu
Keyword(s):  
Moving Average ◽  
Demand Forecasting ◽  
Exponential Smoothing ◽  
Spare Parts ◽  
Next Generation ◽  
Intermittent Demand ◽  
Spare Parts Management ◽  
Single Exponential ◽  
Airline Company

Este estudo tem como objetivo avaliar cinco métodos de previsão para demanda intermitente usando uma série histórica de consumo de peças sobressalentes da aeronave 737 Next Generation, fabricado pela Boeing, da maior frota aérea brasileira gerenciada pela VRG Airline Company S/A. Os métodos de Winter, Croston, Single Exponential Smoothing, Weight Moving Average e Método de Distribuição de Poisson foram testados em um histórico de 53 peças sobressalentes e cada uma delas possui um histórico de demanda de trinta e seis meses (janeiro de 2013 a dezembro de 2015). Os resultados mostraram que os métodos Weight Moving Average, Distribuição de Poisson e Croston apresentaram os melhores ajustes. Além disso, observou-se que a maior parte das demandas por peças sobressalentes apresentaram um padrão smooth ao contrário do resultado obtido pelo estudo de Ghobbar and Friend (2003) que apresentou um padrão lumpy. Por outro lado, tem-se que o Método de Winter apresentou-se como o de pior ajuste em ambos os estudos. Conclui-se que os métodos de Weight Moving Average e Distribuição de Poisson são os mais adequados para avaliar a demanda intermitente para o caso da VRG Airline Company S/A.

A Calculation Model of Gear Steel Hardenability Modeling of Nonlinear Equation

2011 ◽  
Vol 233-235 ◽  
pp. 2352-2355
Author(s):  
Wei Gong ◽  
Zhou Hua Jiang ◽  
Dong Ping Zhan
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Nonlinear Regression ◽  
Forecast Accuracy ◽  
Calculation Model ◽  
Regression Equation ◽  
Linear Regression Equation ◽  
Computer Tools ◽  
Gear Steel ◽  
Better Than

In the paper, the superiority-inferiority in various calculations of hardenability has been compared, and the method of nonlinear regression equation was chosen to establish a mathematical model. The model was modified through the actual production of the gear steel hardenability data. Based on the model, a prediction platform was developed with computer tools to calculate the hardenability of gear steel. The hardenability prediction software can calculate random Jominy distance hardness according to chemical composition and grain size. The forecast accuracy is better than that of unmodified nonlinear regression equation and multiple linear regression equation, but equal to that of artificial neural network.

Forecasting travel demand: a comparison of logit and artificial neural network methods

1998 ◽  
Vol 49 (7) ◽  
pp. 717-722 ◽  
Author(s):  
M C M de Carvalho ◽  
M S Dougherty ◽  
A S Fowkes ◽  
M R Wardman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Travel Demand ◽  
Network Methods ◽  
Artificial Neural

Pencarian Question-Answer Menggunakan Convolutional Neural Network Pada Topik Agama Berbahasa Indonesia

2018 ◽  
Vol 10 (1) ◽  
pp. 57-64 ◽  
Author(s):  
Rizqa Raaiqa Bintana ◽  
Chastine Fatichah ◽  
Diana Purwitasari
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Question Answering ◽  
Mean Average Precision ◽  
Average Precision ◽  
Community Based ◽  
Network Methods ◽  
The Mean ◽  
Index Terms ◽  
Search Information

Community-based question answering (CQA) is formed to help people who search information that they need through a community. One condition that may occurs in CQA is when people cannot obtain the information that they need, thus they will post a new question. This condition can cause CQA archive increased because of duplicated questions. Therefore, it becomes important problems to find semantically similar questions from CQA archive towards a new question. In this study, we use convolutional neural network methods for semantic modeling of sentence to obtain words that they represent the content of documents and new question. The result for the process of finding the same question semantically to a new question (query) from the question-answer documents archive using the convolutional neural network method, obtained the mean average precision value is 0,422. Whereas by using vector space model, as a comparison, obtained mean average precision value is 0,282. Index Terms—community-based question answering, convolutional neural network, question retrieval

A Novel Forecast Method for Air-Conditioning Load of Public Building Considering Accumulated Temperature Effect

2015 ◽  
Vol 9 (1) ◽  
pp. 363-367
Author(s):  
Qingshan Xu ◽  
Xufang Wang ◽  
Chenxing Yang ◽  
Hong Zhu ◽  
Qingguo Yan
Keyword(s):  
Neural Network ◽  
Temperature Effect ◽  
Air Conditioning ◽  
Forecast Accuracy ◽  
Power Network ◽  
Forecast Method ◽  
Elman Neural Network ◽  
Public Building ◽  
Effective Accumulated Temperature ◽  
Accumulated Temperature

It has great significance to estimate the schedulable capacity of air-conditioning load of public building for participating the power network regulation by forecasting the air-conditioning load accurately. A novel forecast method considering the accumulated temperature effect is proposed in this paper based on Elman neural network. Firstly, the starting and ending date for forecast considering the accumulated temperature effect are determined by providing the five day sliding average thermometer algorithm which is usually adopted in aerology research. Then, the effective accumulated temperature of each day is calculated. Finally, take the effective accumulated temperature, temperature and humidity into consideration, the air-conditioning load of public building in the forecast day is acquired by Elman neural network. Simulated results show that the higher forecast accuracy can be achieved by considering the accumulated temperature effect.

scholarly journals CirBiTree: Citrullination Site Inference Based on a Fuzzy Neural Network and Flexible Neural Tree

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Chuandong Song ◽  
Haifeng Wang
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Classification Model ◽  
Peptide Sequence ◽  
Sequence Information ◽  
Post Translational Modification ◽  
Fuzzy Neural ◽  
Human Complex ◽  
Better Than

Emerging evidence demonstrates that post-translational modification plays an important role in several human complex diseases. Nevertheless, considering the inherent high cost and time consumption of classical and typical in vitro experiments, an increasing attention has been paid to the development of efficient and available computational tools to identify the potential modification sites in the level of protein. In this work, we propose a machine learning-based model called CirBiTree for identification the potential citrullination sites. More specifically, we initially utilize the biprofile Bayesian to extract peptide sequence information. Then, a flexible neural tree and fuzzy neural network are employed as the classification model. Finally, the most available length of identified peptides has been selected in this model. To evaluate the performance of the proposed methods, some state-of-the-art methods have been employed for comparison. The experimental results demonstrate that the proposed method is better than other methods. CirBiTree can achieve 83.07% in sn%, 80.50% in sp, 0.8201 in F1, and 0.6359 in MCC, respectively.

scholarly journals End-to-end convolutional neural network enables COVID-19 detection from breath and cough audio: a pilot study

2021 ◽  
Vol 7 (2) ◽  
pp. 356-362
Author(s):  
Harry Coppock ◽  
Alex Gaskell ◽  
Panagiotis Tzirakis ◽  
Alice Baird ◽  
Lyn Jones ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Low Cost ◽  
Area Under The Curve ◽  
Alternative Form ◽  
Economic Damage ◽  
Audio Signals ◽  
Operating Characteristics ◽  
Data Set ◽  
Empirical Performance

BackgroundSince the emergence of COVID-19 in December 2019, multidisciplinary research teams have wrestled with how best to control the pandemic in light of its considerable physical, psychological and economic damage. Mass testing has been advocated as a potential remedy; however, mass testing using physical tests is a costly and hard-to-scale solution.MethodsThis study demonstrates the feasibility of an alternative form of COVID-19 detection, harnessing digital technology through the use of audio biomarkers and deep learning. Specifically, we show that a deep neural network based model can be trained to detect symptomatic and asymptomatic COVID-19 cases using breath and cough audio recordings.ResultsOur model, a custom convolutional neural network, demonstrates strong empirical performance on a data set consisting of 355 crowdsourced participants, achieving an area under the curve of the receiver operating characteristics of 0.846 on the task of COVID-19 classification.ConclusionThis study offers a proof of concept for diagnosing COVID-19 using cough and breath audio signals and motivates a comprehensive follow-up research study on a wider data sample, given the evident advantages of a low-cost, highly scalable digital COVID-19 diagnostic tool.

scholarly journals Multi-Criteria Spare Parts Classification Using the Deep Convolutional Neural Network Method

2021 ◽  
Vol 11 (15) ◽  
pp. 7088
Author(s):  
Ke Yang ◽  
Yongjian Wang ◽  
Shidong Fan ◽  
Ali Mosleh
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Inventory Management ◽  
Hierarchical Classification ◽  
Critical Issue ◽  
Spare Parts ◽  
Deep Convolutional Neural Network ◽  
Learning Ability ◽  
Second Phase ◽  
Good Decision

Spare parts management is a critical issue in the industrial field, alongside planning maintenance and logistics activities. For accurate classification in particular, the decision-makers can determine the optimal inventory management strategy. However, problems such as criteria selection, rules explanatory, and learning ability arise when managing thousands of spare parts for modern industry. This paper presents a deep convolutional neural network based on graph (G-DCNN) which will realize multi-criteria classification through image identification based on an explainable hierarchical structure. In the first phase, a hierarchical classification structure is established according to the causal relationship of multiple criteria; in the second phase, nodes are colored according to their criteria level status so that the traditional numerical information can be visible through graph style; in the third phase, the colored structures are transferred into images and processed by structure-modified convolutional neural network, to complete the classification. Finally, the proposed method is applied in a real-world case study to validate its effectiveness, feasibility, and generality. This classification study supplies a good decision support to improve the monitor-focus on critical component and control inventory which will benefit the collaborative maintenance.

Sign in / Sign up

Export Citation Format

Share Document