Time Series Models Discovery with Similarity-Based Neuro-Fuzzy Networks and Genetic Algorithms: A Parallel Implemention.
Bibliographic record
Abstract
This paper presents a parallel implementation of a hybrid data mining technique for multivariate heterogeneous time varying processes based on a combination of neuro-fuzzy techniques and genetic algorithms. The purpose is to discover patterns of dependency in general multivariate time-varying systems, and to construct a suitable representation for the function expressing those dependencies. The patterns of dependency are represented by multivariate, non-linear, autoregressive models. Given a set of time series, the models relate future values of one target series with past values of all such series, including itself. The model space is explored with a genetic algorithm, whereas the functional approximation is constructed with a similarity based neuro-fuzzy heterogeneous network. This approach allows rapid prototyping of interesting interdependencies, especially in poorly known complex multivariate processes. This method contains a high degree of parallelism at different levels of granularity, which can be exploited when designing distributed implementations, such as workcrew computation in a master-slave paradigm. In the present paper,a first implementation at the highest granularity level is presented. The implementation was tested for performance and portability in different homogeneous and heterogeneous Beowulf clusters with satisfactory results. An application example with a known time series problem is presented.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".