Cluster-Based Symbolic Compression of Time Series for Scalable Forecasting and Analysis
Bibliographic record
Abstract
The increasing volume and velocity of time series data make it very challenging to perform effective and scalable predictive modeling. This paper presents a novel time series compression and transformation pipeline to address this challenge. We have combined three key components in a novel approach: sliding window segmentation for identifying local patterns, centroid-based clustering for converting continuous values into discrete symbols, and run-length encoding for compact representation of similar symbolic sequences. This combination allows us to convert raw time series data into easily understandable numerical symbolic representations, capturing essential time patterns while significantly reducing the volume of data. We utilized real-world metrics from Azure Function system-wide traces, including the number of applications, functions, invocations, and average execution time, which were collected every 5 minutes over a 14-day period, to evaluate the proposed method. Experimental results demonstrate impressive compression ratios while preserving pattern interpretations with lightweight computation, highlighting the method’s effectiveness in reducing the learning cost for regression and forecasting tasks. The resulting model-agnostic representation can be easily applied to a variety of machine learning architectures, including recurrent and transformer-based models, providing an effective solution for scalable time series analytics in various fields, including both cloud and edge computing environments.
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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".