The proposed general framework also shows promising application prospects and paves the way for efficiently improving existing STGNN models with minimal changes. Abstract: Multivariate Time Series ...

this paper presents the BiL-AT method for multivariate time series anomaly detection, which combines bidirectional long short-term memory (BiLSTM) and attention mechanism (AT) to perform dual analysis ...

This repository now supports Structured Linear Controlled Differential Equations (SLiCEs ... per step on the UEA multivariate time-series tasks whilst achieving the same average test accuracy. The ...

We refer to the function in Equation 1 as full ... For the multivariate setting, each method produces predictions based on multiple features over the time series. From Table 6, we observe that: (1) ...

It’s familiar, expressive, and already the language many developers and data engineers use when building logic around time series data. Plus, as large language models improve at generating ...

Tipping points are the death of ecosystems. So scientists watch as warning signs gradually worsen until an ecosystem reaches ...

This research addresses the assessment of real options via the time-fractional Heston model, which takes into account jumps ...

Cleveland Clinic researchers are working to improve the way we use evolutionary modeling to understand drug resistance. The ...

The 2025 Monaco Grand Prix will take place on Sunday, and it's generally considered the biggest event on the Formula 1 schedule. Circuit de Monaco winds through the narrow streets of Monte Carlo ...

Developed by daily Fantasy pro and SportsLine predictive data engineer Mike McClure, this proprietary Formula 1 prediction model simulates every race 10,000 times, taking into account factors such ...