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Material demand forecasting with classical and fuzzy time series models

Sergey Zakrytnoy, Pasi Luukka, Jan Stoklasa
2021 KNOWCON-NSAIS workshop on Business Analytics  
In this paper we are dealing with material demand forecasting and evaluate the feasibility of fuzzy time series forecasting models as compared to classical forecasting models.  ...  Distortion effects in demand projections and overall uncertainty cause the enterprises to rely on internal data to build their forecasts.  ...  ACKNOWLEDGMENT The paper represents a processed summary of the research performed in scope of a Master thesis [18] .  ... 
doi:10.15439/2021b8 dblp:conf/knowcon/ZakrytnoyLS21 fatcat:w544tuiq6vbapnzs52pz3txori

Modifying the Classic Peak Picking Technique Using a Fuzzy Multi Agent to Have an Accurate P300-based BCI

Gholamreza Salimi Khorshidi, Ayyoub Jaafari, Ali Motie Nasrabadi, Mohammadreza Hashemi Golpayeghani
2007 European Society for Fuzzy Logic and Technology  
The proposed model uses more than one scalp electrode and combines the outputs with a fuzzy technique, to detect P300 cognitive component.  ...  EEG-based brain computer interface (BCI) provides a new communication channel between the human brain and a computer. The classification of EEG data is an important task in EEG-based BCI.  ...  Fuzzy rules used to determine the final label of an input trial based on its TM output over electrodes Cz, C3, C4, Fz, F3, and F4.  ... 
dblp:conf/eusflat/KhorshidiJNG07 fatcat:hgygkowaoza37mfw6bxfmo43hm

Exploring the Performance of Tagging for the Classical and the Modern Standard Arabic

Dia AbuZeina, Taqieddin Mostafa Abdalbaset
2019 Advances in Fuzzy Systems  
The performance decline might be an indication of the necessity to distinguish between training data for both classical and MSA Arabic for NLP tasks.  ...  In this paper, we examine the performance of a modern standard Arabic (MSA) based tagger for the classical (i.e., traditional or historical) Arabic.  ...  Advances in Fuzzy Systems  ... 
doi:10.1155/2019/6254649 fatcat:3zx6c46b6re3thlr4n2mjrb3g4

Evaluation of Classical Operators and Fuzzy Logic Algorithms for Edge Detection of Panels at Exterior Cladding of Buildings

Chang Liu, Sara Shirowzhan, Samad M. E. Sepasgozar, Ali Kaboli
2019 Buildings  
The automated process of construction defect detection using non-contact methods provides vital information for quality control and updating building information modelling.  ...  The performance of an image processing algorithm depends on the quality of images and the algorithm utilised.  ...  Acknowledgments: The equipment and tools are supported by an education project called Mixed Reality Construction Learning at the University of New South Wales.  ... 
doi:10.3390/buildings9020040 fatcat:zeilfmukxvfrjewqbezuihuvwe

Quantum-to-classical transition via fuzzy measurements on high-gain spontaneous parametric down-conversion

Chiara Vitelli, Nicolò Spagnolo, Lorenzo Toffoli, Fabio Sciarrino, Francesco De Martini
2010 Physical Review A. Atomic, Molecular, and Optical Physics  
dichotomization processes.  ...  The possibility of observing quantum correlations in such macroscopic quantum system through dichotomic measurement will be analyzed by addressing two different measurement schemes, based on different  ...  In this section we analyze two possible kinds of dichotomic measurements of macroscopic states, based on photon counting and signal processing techniques.  ... 
doi:10.1103/physreva.81.032123 fatcat:atpmzyxxhvffxogcaqhtsikhjy

(Meta-)data modelling

Florence Sèdes, Franck Jeveme Panta
2017 SIGSPATIAL Special  
The context of this paper is related to indoor locations systems based on wireless cell, ICCARD sensors and video surveillance cameras.  ...  In order to manage them in a given framework, it is necessary to homogenize the relevant (Meta) data to process the global knowledge they can give.  ...  During the query processing, the inclusion of the uncertainty is capital (temporal fuzzy and spatial fuzzy) [26] .  ... 
doi:10.1145/3124104.3124111 fatcat:nw3prsmmdjbclhzpdqdfgayjoq

Hydrochemical Characterization of an Acid Mine Effluent from Concepcion Mine Using Classical Statistic and Fuzzy Logic Techniques

María Santisteban, Ana Teresa Luís, José Antonio Grande, Javier Aroba, José Miguel Dávila, Aguasanta Miguel Sarmiento, Juan Carlos Fortes, Francisco Cordoba, Ángel Mariano Rodriguez-Pérez
2022 Minerals  
through the application of Fuzzy Logic and classical statistics tools.  ...  The interdependent relationship between the measured physicochemical parameters are set in order to propose a model, capable of describing the evolution of contaminants in response to the processes and  ...  In this study, a methodology based on the use of the data mining technique PreFuRGe (Predictive Fuzzy Rules Generator) [15] , is proposed for data treatment.  ... 
doi:10.3390/min12040464 fatcat:mn4uccjxjbaurjde2zfdr5buiu

A Linear Regression Model for Nonlinear Fuzzy Data [chapter]

Juan Carlos Figueroa-García, Jesus Rodriguez-Lopez
2012 Lecture Notes in Computer Science  
Two examples are solved through different approaches followed by a goodness of fit statistical analysis based on the measurement of the residuals of the model. Corresponding authors.  ...  A fuzzy linear programming model has been designed to solve the problem with nonlinear fuzzy data by combining the fuzzy arithmetic theory with convex optimization methods.  ...  An LP model is designed based on the interesting values.  ... 
doi:10.1007/978-3-642-24553-4_47 fatcat:cc6asitjfjgbvnhn3tvvdqy5cu

Far beyond the classical data models: symbolic data analysis

Monique Noirhomme-Fraiture, Paula Brito
2011 Statistical analysis and data mining  
This paper introduces symbolic data analysis, explaining how it extends the classical data models to take into account more complete and complex information.  ...  Some methods for the (multivariate) analysis of symbolic data are presented and discussed.  ...  Sónia Dias, from the University of Porto, for kindly providing the data on healthcare centers, used in Section 3.  ... 
doi:10.1002/sam.10112 fatcat:whhrw24bnjaq3c4kkhkwyawvce

Model-based Data Fusion in Industrial Process Instrumentation [chapter]

Gerald Steiner
2009 Sensor and Data Fusion  
The knowledge then contained in the process model can be fruitfully exploited in model-based data fusion.  ...  Some of the key requirements on process instrumentation are: operation under harsh and varying environmental conditions, high reliability, fault tolerance, and low cost.  ...  Fig. 4 . 4 Membership functions used for fuzzy-based fault detection and isolation. First the states and covariance are projected to the next time step based on the process model.  ... 
doi:10.5772/6579 fatcat:djx6mtkvgjbxbfiyv7bfhdodhm

Evaluating performance supply chain by a new non-radial network DEA model with fuzzy data

Mohsen Rostamy-Malkhalifeh, Elahe Mollaeian
2012 Data Envelopment Analysis and Decision Science  
Its optimal solution can separate inefficient and strong efficient DMUs, and finally we solve this model when the all data are fuzzy numbers.  ...  But in the real word, we are often conformed to vague and uncertain data and performance evaluation by usual methods in the presence such data may lead errors in decision-making process, so for making  ...  The assumption of DEA was based on the exact data, But in the real word, data presented by natural languages including good, bad,… .  ... 
doi:10.5899/2012/dea-00005 fatcat:ip4qv4xc3nds5g3w3wd7f4bbte

A Network-Based Data Envelope Analysis Model in a Dynamic Balanced Score Card

Mojtaba Akbarian, Esmaeil Najafi, Reza Tavakkoli-Moghaddam, Farhad Hosseinzadeh-Lotfi
2015 Mathematical Problems in Engineering  
In this paper, an integrated framework of the BSC and DEA models is proposed for measuring the efficiency during the time and along with strategies based on the time delay of the lag key performance indicators  ...  To assess the performance, a balanced score card (BSC) along with strategic goals and a data envelopment analysis (DEA) are used as powerful qualitative and quantitative tools, respectively.  ...  Additionally, the authors thank the National Iranian Oil Refining & Distribution Company for providing the real data.  ... 
doi:10.1155/2015/914108 fatcat:tg4s4bbkcvd4njz46y3rjp7ajq

A robust fuzzy k-means clustering model for interval valued data

Pierpaolo D'Urso, Paolo Giordani
2006 Computational statistics (Zeitschrift)  
In this paper a robust fuzzy k-means clustering model for interval valued data is introduced.  ...  In order to show how our model works, the results of some applications to synthetic and real interval valued data are discussed.  ...  Ralambondrainy (1995) proposed a conceptual k-means clustering method for mixed data (with numerical and symbolic features) based on coding symbolic data numerically and using a mix of Euclidean and Chi-square  ... 
doi:10.1007/s00180-006-0262-y fatcat:jrcqtq4oefas3ds5aovpsrfjga

A Formal Model to Compute Uncertain Continuous Data [chapter]

Jérôme Dantan, Yann Pollet, Salima Taibi
2016 First Complex Systems Digital Campus World E-Conference 2015  
Current researches in the domain of Information and Communication Technologies describe and extend the existing formalisms to develop systems that compute uncertain data.  ...  Indeed, handling uncertain data is a great challenge for complex systems. In this article, we provide a formal model to compute such data rigorously.  ...  But increasing amounts of data have to be processed so that there is not enough time for data cleaning step. Decisions of experts from various fields are based on aggregations of data.  ... 
doi:10.1007/978-3-319-45901-1_8 fatcat:x63vxquokjbylaghmxc4y2qesi

Stock Price Prediction based on Data Mining Combination Model

2022 Journal of Global Information Management  
This work uses neural network, support vector machine (SVM), mixed data sampling (MIDAS), and other methods in data mining technology to predict the daily closing price of the next 20 days and the monthly  ...  Combining value investment effectively with nonlinear models, a complete stock forecasting model is established, and empirical research is conducted on it.  ...  MIDAS Model At present, the most commonly used model for data processing is the mixed data sampling model(MIDAS).  ... 
doi:10.4018/jgim.296707 fatcat:gyyuidcrnjgahpigakhtzyuei4
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