Twin learning for domain agnostic time series analysis: A regime-switch approach
Bibliographic record
Abstract
Correlations among variables in complex ecosystems such as weather systems and financial markets result in large amounts of dynamic and co-evolving time sequences. The benefits of discovering and predicting intricate patterns (aka regimes) in time sequences are multifold, including better understanding of the ecosystem dynamics, optimizing model selection, and improving interpretability of results. Despite recent advancements, existing methods primarily emphasize predictive accuracy, which might overshadow the need to comprehend the structural dynamics within the series. Additionally, these methods often encounter one or more of the following limitations: (1) difficulty in identifying regimes within domain-dependent segmentations; (2) inability to integrate nonlinear relationships across time series; (3) lack of an effective method to encapsulate the temporal behaviors. To tackle these challenges, we introduce a twin learning regime-switch model to simultaneously learn domain-agnostic segmentation and regime switch in a principled way. Specifically, we devise a kernel-based method that determines the duration of regime and captures dynamic switches through potent representations, accounting for the non-linear interactions between series. With this model, it is feasible to automatically achieve the two subtasks of identifying the optimal regimes and determining the most suitable segmentation. Experimental results on synthetic and real-world datasets indicate that our method is capable of revealing the structures that underpin the behavior of co-evolving ecosystems, which display different dynamics. These structures can be leveraged to better define regimes with superior predictive capabilities compared to widely used traditional models and state-of-the-art neural network models. • We propose a novel regime-switch model to identify domain-agnostic regimes in time series. • We develop a kernel representation learning approach to capturing dynamic regime switches. • This representation allows effectively modeling nonlinear interactions and co-evolving patterns in time series. • Our model transforms heavy sets of time series into a lighter and meaningful structure, enabling a deeper understanding of structural dynamics. • The extensive experimental results showcase superior predictive performance compared to traditional and state-of-the-art methods.
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 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".