Configurable FPGA-Based Outlier Detection for Time Series Data
Bibliographic record
Abstract
An outlier detection technique based on joint estimation of model parameters and outlier effects in time series is implemented in FPGA-based configurable real-time outlier detection hardware. The hardware models time series data with an autoregressive-moving-average (ARMA) process and identifies the outliers based on test statistics using unbiased parameters. A configurable hardware implementation was written in Verilog, simulated to verify its correctness, and synthesized on an Altera FPGA device from the Stratix V family. The design is configurable by adjusting the number of iterations for the optimization process, the number of samples in the time series data, and the critical value. A reported configuration of this design has a total power dissipation of 1.14W, while processing 35 million data points per second, giving an energy usage of 32 nJ per processed data point. The implemented hardware is capable of detecting multiple additive outliers in time series data with a detection accuracy of 99% and has a type I error rate of 1.05%. Compared to a general purpose CPU running a software implementation of the outlier detection algorithm, the FPGA implementation reduces the power consumption by 89% at similar rates of data throughput.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".