Benchmarking Deep Legendre-SNN for Time Series Classification - Analysis and Enhancements
Bibliographic record
Abstract
Compute-and energy-efficient Time Series Classification (TSC) is the need of the hour - to cater the continually growing sources and applications of temporal data. State-of-the-Art (SoTA) temporal computational models, e.g., LSTMs/RNNs, HIVE-COTE, Transformers, etc., are high performing, but are also resource intensive, resulting in high energy consumption on CPUs/GPUs. On the contrary, Reservoir Computing (RC) based models are resource-efficient and perform well for simple TSC datasets; and when implemented with spiking neurons, spiking RC-based models offer the promise of high energy-efficiency on neuromorphic hardware. In this work, we analyse, enhance, and benchmark the newly introduced-spiking RC-based, "Legendre Spiking Neural Network" (Legendre-SNN or LSNN) model for TSC. We theoretically investigate the Legendre Delay Network (LDN) that acts as a reservoir in the LSNN model, and bring some useful insights into the design of the LDN-based models. In our analysis, we find that a higher order LDN is necessary for optimal performance with input signals composed of higher frequencies. We also extend the existing LSNN model to multivariate timeseries signals and propose the "DeepLSNN" model. We conduct experiments with DeepLSNN on 102 benchmark TSC-datasets (comprising both univariate and multivariate signals). Via such large scale experiments, we present the first benchmark-results for spiking-TSC. Considering DeepLSNN's best results, we find that it outperforms the non-spiking LSTM-FCN on more than 31% of the 102 datasets. We note that our benchmark-results can serve as a comparison criterion for other spiking-TSC experiments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".