Learning Hyper-Parameters of Image Transformations for Time Series Classification
Bibliographic record
Abstract
Time Series Classification (TSC) is the task of using a time series to predict the value of a categorical variable-the class of the time series. An interesting approach that has been studied in the TSC literature is converting a given time series into an image, through one of various proposed methods, and classifying the resultant image through a vision model. Furthermore, existing image transformation methods may require the values of one or more parameters. Modifying these parameter values can greatly alter the generated image, thereby impacting the performance of a vision-based classifier. However, guidelines for selecting appropriate parameter values have not been detailed. In fact, due to the diversity of time series used for TSC tasks, establishing such guidelines in a way that ensures optimal results may be impossible. Following this realization, we attempt to build Deep Learning models capable of independently learning appropriate parameter values to maximize classification accuracy. We propose a method wherein these parameters are dynamically determined through a Policy Network, which takes a time series as input. Then, an image is obtained using the resultant parameters and a pre-determined image transformation function. Finally, the image is fed into a vision model to obtain the class of the time series. This approach was tested for three pre-existing image transformation functions on the entirety of the UCR Archive of 128 TSC datasets. The results demonstrate that it may be more effective to allow models to learn the parameters of image transformation functions instead of setting them beforehand.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| 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".