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A stochastic time series generator with adaptive software architecture

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

Why is this work in the frame?

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

Canadian affiliationAn author listed a Canadian institution. This is the only route the usual frame has.

The three-model screen

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All three models called this out of scope.

stratum: aff_core · design weight: 5595.24 (the sample is stratified; any rate computed without the weight is wrong)
Claude Opus 4.8OUT
genre: infrastructure/announcement
about Canada: no
confidence: 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
about Canada: no
confidence: high

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

Grok 4.5OUT
genre: empirical
about Canada: no
confidence: high

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

Abstract

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.

Stored with the screening record, where it is evidence for the labels above.

The record

Venue
Topic
Time Series Analysis and Forecasting
Field
Computer Science
Canadian institutions
University of Calgary
Funders
Keywords
Series (stratigraphy)Computer scienceComponent (thermodynamics)Time seriesGenerator (circuit theory)SoftwareStochastic processAlgorithmStochastic modellingData miningMathematicsStatisticsMachine learningProgramming languagePower (physics)
Has abstract in OpenAlex
yes