A step-by-step guide to catching real anomalies without drowning in false alerts.
This paper provides a theoretical analysis of Transformers' limitations for time series forecasting through the lens of In-Context Learning (ICL) theory, demonstrating that even powerful Transformers often fail to outperform simpler models like linear models. The study focuses on Linear Self-Attention (LSA) models and shows that they cannot achieve lower expected MSE than classical linear models for in-context forecasting, and that predictions collapse to the mean exponentially under Chain-of-Thought inference.
This article explores how prompt engineering can be used to improve time-series analysis with Large Language Models (LLMs), covering core strategies, preprocessing, anomaly detection, and feature engineering. It provides practical prompts and examples for various tasks.
This article details a hands-on approach to modeling rare events in time series data using Python. It covers data exploration, defining extreme events, fitting distributions (GEV, Weibull, Gumbel), and evaluating model performance using metrics like log-likelihood, AIC, and BIC. The example uses weather data and provides code snippets for implementation.
An article discussing the importance of time series databases and data visualization tools like Grafana for managing and interpreting streams of data in various applications.
The author mentions several time series databases (TSDs) and visualization tools, focusing on their features, advantages, and some limitations. The article also provides an example of a Building Management and Control (BMaC) project that uses InfluxDB and Grafana for data visualization.
| Database | Description | Notable Features |
|-------------------|-------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------|
| InfluxDB | Partially open source, with version 3 being an edge data collector. | Shard-based storage, compaction levels, time series index, optional retention. |
| Apache Kudu | Column-based database optimized for multidimensional OLAP workloads. | Part of the Apache Hadoop ecosystem. |
| Prometheus | Developed at SoundCloud for metrics monitoring. | Written in Go, similar to InfluxDB v1 and v2. |
| RRDTool | All-in-one package with a circular buffer TSD that also does graphing. | Language bindings for various programming languages. |
| Graphite | Similar to RRDTool but uses a Django web-based application to render graphs. | Web-based graphing. |
| TimescaleDB | Extends PostgreSQL, supporting typical SQL queries with TSD functionality and optimizations. | Supports all typical SQL queries. |
The article also discusses Grafana as a popular tool for creating dashboards to visualize time series data, mentioning its compatibility with multiple TSDs and SQL databases. It concludes by highlighting the importance of understanding one's specific needs before choosing a TSD and visualization solution.
The author mentions several time series databases (TSDs) and visualization tools, focusing on their features, advantages, and some limitations. The article also provides an example of a Building Management and Control (BMaC) project that uses InfluxDB and Grafana for data visualization.
| Database | Description | Notable Features |
|-------------------|-------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------|
| InfluxDB | Partially open source, with version 3 being an edge data collector. | Shard-based storage, compaction levels, time series index, optional retention. |
| Apache Kudu | Column-based database optimized for multidimensional OLAP workloads. | Part of the Apache Hadoop ecosystem. |
| Prometheus | Developed at SoundCloud for metrics monitoring. | Written in Go, similar to InfluxDB v1 and v2. |
| RRDTool | All-in-one package with a circular buffer TSD that also does graphing. | Language bindings for various programming languages. |
| Graphite | Similar to RRDTool but uses a Django web-based application to render graphs. | Web-based graphing. |
| TimescaleDB | Extends PostgreSQL, supporting typical SQL queries with TSD functionality and optimizations. | Supports all typical SQL queries. |
The article also discusses Grafana as a popular tool for creating dashboards to visualize time series data, mentioning its compatibility with multiple TSDs and SQL databases. It concludes by highlighting the importance of understanding one's specific needs before choosing a TSD and visualization solution.
IBM’s new foundation model, TSPulse, can go beyond standard forecasting tasks to detect anomalies, fill in missing values, classify data, and search recurring patterns. It’s also tiny enough to run on a laptop.
This article demonstrates how to use the attention mechanism in a time series classification framework, specifically for classifying normal sine waves versus 'modified' (flattened) sine waves. It details the data generation, model implementation (using a bidirectional LSTM with attention), and results, achieving high accuracy.
A machine learning library for unsupervised time series anomaly detection. Orion provides verified ML pipelines to identify rare patterns in time series data.
SigLLM is an extension of the Orion library, built to detect anomalies in time series data using LLMs. It provides two types of pipelines for anomaly detection: Prompter (directly prompting LLMs) and Detector (using LLMs to forecast time series).
This paper introduces Toto, a time series forecasting foundation model with 151 million parameters, and BOOM, a large-scale benchmark for observability time series data. Toto uses a decoder-only architecture and is trained on a large corpus of observability, open, and synthetic data. Both Toto and BOOM are open-sourced under the Apache 2.0 License.
