Complexity-Based Lambda Layer for Time Series Prediction
Bibliographic record
Abstract
Time series analysis forms the basis for the temporal sequences that we observe in everyday natural phenomenon. Examining and characterizing time series' forms the basis for research in areas spanning classifying heart rate variability, temperature prediction and stock price prediction. In recent history combining powerful techniques such as Fourier Analysis with Machine Learning techniques has improved our ability to predict future time series based on historic data. Complexity is one of such tools that incorporates long-range dependency based on the inherent self-similarity that exists in many natural phenomena. Leveraging the prowess of recurrent neural networks with that of complexity measures combines two very powerful techniques to improve prediction accuracy. In this work a Complexity Lambda Layer was initialized in series with Artificial Neural Networks (ANN) architectures to improve prediction accuracy for synthesized Brownian Noise. Window size for recurrence and the stationary interval size was optimized for increased performance. For both non-temporal and temporal studies, a 2-4 fold improvement in root-mean-squared accuracy was obtained. This approach was implemented as a Lambda layer, meaning the improvement can be done on-the fly with minimal overhead.
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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 | 0.000 |
| 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) | 0.005 | 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".