MétaCan
← tous les travaux

A stochastic time series generator with adaptive software architecture

2010· article· en· 2 citations· W1997129076 sur OpenAlex· 10.1109/iri.2010.5558928

Pourquoi ce travail est-il dans la base ?

Une base qui oublie comment elle a trouvé un travail ne peut pas être vérifiée. Voici les voies qui ont admis celui-ci.

Affiliation canadienneUne personne signataire a déclaré un établissement canadien. C'est la seule voie dont dispose la base habituelle.

Le tri à trois modèles

les 1 000 travaux triés →

Les trois modèles l'ont jugé hors champ.

strate : aff_core · poids de sondage : 5595.24 (l'échantillon est stratifié ; tout taux calculé sans le poids est faux)
Claude Opus 4.8OUT
genre : infrastructure/announcement
porte sur le Canada: non
confiance: medium

Software implementation of a stochastic hydrologic time series generator; a domain modeling tool, not scholarly research infrastructure.

GPT-5.6 (high)OUT
genre : empirical
porte sur le Canada: non
confiance: high

The paper presents a hydrologic time-series generator rather than studying research practice.

Grok 4.5OUT
genre : empirical
porte sur le Canada: non
confiance: high

Hydrologic time-series software tool; uses computing for domain data, does not study research infrastructure as such.

Résumé

Stochastic time series are preferred to historic data series of shorter duration since they contain sequences that may not be observed in a relatively short historic record. Algorithms to generate stochastic time series from historic data have already been proposed. In this paper we present an implementation of an efficient stochastic time series generation algorithm and a component based front-end software system for it. The algorithm is built as three distinct and customizable components. The component based architecture allows for seamless selection of the processing steps as well as integration of new algorithms. The system has been tested successfully on several numerical experiments using hydrologic time series data to generate lengthy (1000 years) of weekly or monthly river flows for multiple locations such that all relevant statistics of the historic series are preserved in the generated series.

Conservé avec la notice de tri, où il sert de preuve aux étiquettes ci-dessus.

La notice

Revue
Thématique
Time Series Analysis and Forecasting
Domaine
Computer Science
Établissements canadiens
University of Calgary
Organismes subventionnaires
Mots-clés
Series (stratigraphy)Computer scienceComponent (thermodynamics)Time seriesGenerator (circuit theory)SoftwareStochastic processAlgorithmStochastic modellingData miningMathematicsStatisticsMachine learningProgramming languagePower (physics)
Résumé présent dans OpenAlex
oui