Search results for "Data transformation"

showing 8 items of 8 documents

Holt–Winters Forecasting: An Alternative Formulation Applied to UK Air Passenger Data

2007

Abstract This paper provides a formulation for the additive Holt–Winters forecasting procedure that simplifies both obtaining maximum likelihood estimates of all unknowns, smoothing parameters and initial conditions, and the computation of point forecasts and reliable predictive intervals. The stochastic component of the model is introduced by means of additive, uncorrelated, homoscedastic and Normal errors, and then the joint distribution of the data vector, a multivariate Normal distribution, is obtained. In the case where a data transformation was used to improve the fit of the model, cumulative forecasts are obtained here using a Monte-Carlo approximation. This paper describes the metho…

Statistics and ProbabilityExponential smoothingData transformation (statistics)Prediction intervalMultivariate normal distributionJoint probability distributionHomoscedasticityStatisticsEconometricsStatistics Probability and UncertaintyTime seriesPhysics::Atmospheric and Oceanic PhysicsSmoothingMathematicsJournal of Applied Statistics
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Evolution-Oriented User-Centric Data Warehouse

2011

Data warehouses tend to evolve, because of changes in data sources and business requirements of users. All these kinds of changes must be properly handled, therefore, data warehouse development is never-ending process. In this paper we propose the evolution-oriented user-centric data warehouse design, which on the one hand allows to manage data warehouse evolution automatically or semi-automatically, and on the other hand it provides users with the understandable, easy and transparent data analysis possibilities. The proposed approach supports versions of data warehouse schemata and data semantics.

Business requirementsDatabaseComputer scienceProcess (engineering)Data transformationInformationSystems_DATABASEMANAGEMENTDimensional modelingcomputer.software_genrecomputerData warehouseUser-centered designData semantics
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Forecasting time series with missing data using Holt's model

2009

This paper deals with the prediction of time series with missing data using an alternative formulation for Holt's model with additive errors. This formulation simplifies both the calculus of maximum likelihood estimators of all the unknowns in the model and the calculus of point forecasts. In the presence of missing data, the EM algorithm is used to obtain maximum likelihood estimates and point forecasts. Based on this application we propose a leave-one-out algorithm for the data transformation selection problem which allows us to analyse Holt's model with multiplicative errors. Some numerical results show the performance of these procedures for obtaining robust forecasts.

Statistics and ProbabilityApplied MathematicsAutocorrelationExponential smoothingLinear modelData transformation (statistics)EstimatorMissing dataExpectation–maximization algorithmStatisticsStatistics Probability and UncertaintyAdditive modelAlgorithmMathematicsJournal of Statistical Planning and Inference
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Technology-Supported Guidance Models Stimulating the Development of Critical Thinking in Clinical Practice: Protocol for a Mixed Methods Systematic R…

2020

BackgroundCritical thinking is an essential skill that nursing students need to develop. Technological tools have opened new avenues for technology-supported guidance models, but the challenges and facilitators of such guidance models, as well as how they stimulate the development of critical thinking, remain unclear.ObjectiveWe developed a protocol for a mixed methods systematic review to investigate the use of technology-supported guidance models that stimulate the development of critical thinking in nursing education clinical practice.MethodsA convergent integrated design following the Joanna Briggs Institute Manual for Evidence Synthesis will be employed. A pair of authors will select t…

Data transformationComputer applications to medicine. Medical informaticsguidance modelsR858-859.703 medical and health sciences0302 clinical medicineProtocolcritical thinking030212 general & internal medicineNurse educationProtocol (science)Medical education030504 nursingClinical study designnursing educationRGeneral Medicineclinical practiceClinical PracticeCritical thinkingData extractiontechnologyMedicine0305 other medical sciencePsychologyEvidence synthesisJMIR Research Protocols
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Combining Inter-Subject Modeling with a Subject-Based Data Transformation to Improve Affect Recognition from EEG Signals

2019

Existing correlations between features extracted from Electroencephalography (EEG) signals and emotional aspects have motivated the development of a diversity of EEG-based affect detection methods. Both intra-subject and inter-subject approaches have been used in this context. Intra-subject approaches generally suffer from the small sample problem, and require the collection of exhaustive data for each new user before the detection system is usable. On the contrary, inter-subject models do not account for the personality and physiological influence of how the individual is feeling and expressing emotions. In this paper, we analyze both modeling approaches, using three public repositories. T…

Normalization (statistics)Data AnalysisSupport Vector MachineDatabases FactualComputer sciencemedia_common.quotation_subjectEmotionsData transformation (statistics)Context (language use)02 engineering and technologyvalence detectionElectroencephalographyAffect (psychology)Machine learningcomputer.software_genrelcsh:Chemical technologyBiochemistryModels BiologicalArticleAnalytical Chemistrydata transformation0202 electrical engineering electronic engineering information engineeringmedicinePersonalityHumanslcsh:TP1-1185EEGElectrical and Electronic EngineeringInstrumentationarousal detectionmedia_commonmedicine.diagnostic_testbusiness.industry020206 networking & telecommunicationsSubject (documents)ElectroencephalographySignal Processing Computer-AssistedAtomic and Molecular Physics and Opticsnormalization020201 artificial intelligence & image processingArtificial intelligencebusinessArousalcomputerSensors
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Quality of Service Management on Multimedia Data Transformation into Serial Stories Using Movement Oriented Method

2011

Multimedia data transformation into serial stories or story board will help to reduce the consumption of storage media, indexing, sorting and searching system. Movement Oriented Method that is being developed changes the form of multimedia data into serial stories. Movement Oriented Method depends on the knowledge each actor who uses it. Different knowledge of each actor in the transformation process raises complex issues, such as the sequence, and the resulted story object that could become the standard. And the most fatal could be, the resulted stories does not same with the original multimedia data. To solve it, the Standard Level Knowledge (SLK) in maintaining the quality of the story c…

General Computer ScienceMultimediaProcess (engineering)Computer scienceQuality of servicemedia_common.quotation_subjectSearch engine indexingData transformationObject (computer science)computer.software_genreTransformation (function)Quality (business)computermedia_commonInternational Journal of Advanced Computer Science and Applications
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Executable Data Quality Models

2017

The paper discusses an external solution for data quality management in information systems. In contradiction to traditional data quality assurance methods, the proposed approach provides the usage of a domain specific language (DSL) for description data quality models. Data quality models consists of graphical diagrams, which elements contain requirements for data object's values and procedures for data object's analysis. The DSL interpreter makes the data quality model executable therefore ensuring measurement and improving of data quality. The described approach can be applied: (1) to check the completeness, accuracy and consistency of accumulated data; (2) to support data migration in c…

Computer scienceData transformation02 engineering and technologycomputer.software_genreData modeling0203 mechanical engineering0202 electrical engineering electronic engineering information engineeringInformation systemLogical data modelGeneral Environmental ScienceData elementDatabaseInformation qualityData warehouseData mapping020303 mechanical engineering & transportsData modelData qualityGeneral Earth and Planetary Sciences020201 artificial intelligence & image processingData pre-processingData architectureData miningSoftware architecturecomputerData migrationData virtualizationProcedia Computer Science
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COMPARING EVOLVABILITIES: COMMON ERRORS SURROUNDING THE CALCULATION AND USE OF COEFFICIENTS OF ADDITIVE GENETIC VARIATION

2012

In 1992, David Houle showed that measures of additive genetic variation standardized by the trait mean, CVA (the coefficient of additive genetic variation) and its square (IA), are suitable measures of evolvability. CVA has been used widely to compare patterns of genetic variation. However, the use of CVAs for comparative purposes relies critically on the correct calculation of this parameter. We reviewed a sample of quantitative genetic studies, focusing on sire models, and found that 45% of studies use incorrect methods for calculating CVA and that practices that render these coefficients meaningless are frequent. This may have important consequences for conclusions drawn from comparative…

GeneticsSireData transformation (statistics)BiologyHeritabilityQuantitative trait locusEvolvabilityGenetic variationStatisticsGeneticsTraitGeneral Agricultural and Biological SciencesEcology Evolution Behavior and SystematicsSelection (genetic algorithm)Evolution
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