Time Series

Time series forecasting is one of the most important applications of machine learning — from demand prediction, through infrastructure monitoring, to flood forecasting. The problem? Standard models optimize for typical cases. Yet it’s precisely the atypical ones — extreme events — that are often most important to predict. M²FMoE is a model that learns to predict both. The Problem: Extreme Events Break Standard Models Time series forecasting has made remarkable progress. Transformers, frequency-domain methods, and hybrid architectures achieve impressive results on benchmarks. But there’s a catch. ...

Can computers learn to “hear” the rhythm in a stream of data, much like we hear the rhythm in music? And by using this skill, can they better protect us from equipment failures, financial fraud, or health problems? A new scientific paper titled “CaPulse: Detecting Anomalies by Tuning in to the Causal Rhythms of Time Series” attempts to answer these questions. The Problem with Anomalies We live in a world of data. From our heartbeats and stock market fluctuations to energy consumption in a smart city—all of this is time series data, collected at regular intervals. Often lurking within this data are anomalies: strange, unexpected events that can signal a problem. This could be a sudden cardiac arrhythmia, a suspicious bank transaction, or an impending engine failure in a factory. ...