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Record W1538008266 · doi:10.1155/2015/918045

Advanced Time Series Forecasting Methods

2015· article· en· W1538008266 on OpenAlexaffabout
Mehdi Khashei, Çağdaş Hakan Aladağ, İ.B. Türkşen, Ufuk Yolcu

Bibliographic record

VenueMathematical Problems in Engineering · 2015
Typearticle
Languageen
FieldComputer Science
TopicTime Series Analysis and Forecasting
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSeries (stratigraphy)Time seriesComputer scienceEconometricsMathematicsMachine learningGeology

Abstract

fetched live from OpenAlex

1Department of Statistics, Faculty of Arts and Science, Marmara University, Goztepe Campus, Kadikoy, 34700 Istanbul, Turkey 2Department of Industrial Engineering, Faculty of Science, Isfahan University of Technology (IUT), Isfahan 84156-83111, Iran 3Knowledge/Intelligence Systems Laboratory, Department of Mechanical & Industrial Engineering, University of Toronto, Toronto, ON, Canada M5S 3G8 4Department of Statistics, Faculty of Science, Ankara University, 06100 Ankara, Turkey

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.003

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.035
GPT teacher head0.260
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations10
Published2015
Admission routes2
Has abstractyes

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