Learn how to use Autoencoders to detect anomalies in time series data in a few lines of code.
MIT researchers have developed a method using large language models to detect anomalies in complex systems without the need for training. The approach, called SigLLM, converts time-series data into text-based inputs for the language model to process. Two anomaly detection approaches, Prompter and Detector, were developed and showed promising results in initial tests.
Stumpy is a Python library designed for efficient analysis of large time series data. It uses matrix profile computation to identify patterns, anomalies, and shapelets. Stumpy leverages optimized algorithms, parallel processing, and early termination to significantly reduce computational overhead.
Due to temporary inabilities of the models to match the real values with the predictions, random spikes can arise in the “alarm” time series. Thus, in order to make the alarm system more reliable, we use a two-level structure: this first alarm, the one defined above, is merely a warning signal and is processed again to produce a more accurate second level alarm signal. Using a Moving Aggregation node, the moving averages are calculated on a backward window of 21 samples of the level 1 alarm (warning) signals. This moving average operation smooths out all short random spikes in the level 1 alarm time series, retaining only the ones that persist over time.
