WARA AI TRICS: Time Series Challenge
WARA AI TRICS aims to bring real-world time series problems to the academic community, recognizing their cross-domain relevance in modern industrial, telecommunication, and financial systems. To foster methodological innovation and build a sustained research community around time series, we present the first WARA AI-TRICS Open Challenge for researchers in Sweden, with the intention of establishing the challenge as a recurring annual event. Each edition will bring new real-world problems and industry partners, providing a continuous platform for collaboration.
Following each annual challenge, WARA will also publish a Challenge Results paper summarizing the benchmark, participating approaches, and empirical findings. The best-performing teams and participants with particularly novel ideas will be invited to contribute, providing a recurring opportunity to turn challenge work into research publications and establish a citable research contribution. This challenge is open to all PhD and postdoctoral researchers in Sweden, with computational infrastructure and compute resources provided by WARA AI-TRICS.
Participating teams (recommended max of 4 to 6 people) will have a 10-week development period, spanning from October 1, 2026 to December 13, 2026.
Traditional time series forecasting models often operate primarily on the historical observations of a target signal. However, in complex real-world dynamics, a single time series rarely contains enough information to explain all structural shifts, tail events, or localized anomalies. A sudden deviation, peak, or drop in a target signal is frequently driven by underlying contextual regimes, interactions with related signals, or external multimodal information.
To demonstrate the applicability and generality of this paradigm, participants will tackle solutions across three distinct, real-world domain case studies:
- Quantitative Finance (SEB): Modeling structural asset price deviations using an auxiliary regime variates dataset containing macroeconomics, rates, and volatility signals.
- Telecom Systems (Ericsson): Forecasting critical network Key Performance Indicator (KPI) behavior by capturing spatial, temporal, and contextual interactions across neighboring cells, concurrent KPIs, alarms, logs, and topology.
- Industrial Machines Monitoring (IPercept): Anomaly detection in repetitive industrial robot motion using time series foundation models. A robotic system executes a structured motion pattern under controlled, repeatable conditions. Participants build models that learn normal motion from a large unlabeled corpus and detect mechanical degradation patterns in a held-out evaluation window.
Further information on cloud compute access, and submission portal will be announced here.
Submission date: December 13, 2026, 23:59 CET