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Machine Learning Based Tesla Stock Analysis: A Theosophical Review

2023· review· en· W4376607071 on OpenAlexaff
Mamta B Savadatti, Sandhikadiyam Mahendra, P Rohit, R Sachin, Shankar Siva, C Shreya, Chayanath

Bibliographic record

Venuenot available
Typereview
Languageen
FieldDecision Sciences
TopicStock Market Forecasting Methods
Canadian institutionsHorizon College and Seminary
Fundersnot available
KeywordsStock (firearms)Computer scienceStock marketStock priceArtificial intelligenceEconometricsMachine learningEconomicsEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

The paper aims at providing a comparative analysis on stock price determination of tesla company using Machine Learning Algorithms. The stock market is a very competitive environment where the determination of the future value will be highly beneficial to the share holder. By providing a solution to this we plan in cutting down on a certain percentage of risk factor that arises when buying and selling shares.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.047
metaresearch head score (Gemma)0.122
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Bibliometrics, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.984
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0470.122
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0080.005
Bibliometrics0.0030.023
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0030.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.004

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 head0.449
GPT teacher head0.542
Teacher spread0.092 · 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; both teacher heads agree on what is shown here.

Study designOther design
Domainnot available
GenreReview

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

Citations0
Published2023
Admission routes1
Has abstractyes

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