TAIS is a research collective studying temporal modeling, agent systems, and applied intelligence through reproducible papers, prototypes, and datasets.
We use sequence as a common language across forecasting, generative modeling, Agent trajectories, and embodied interaction.
A time series, a tool-using Agent, and a robot in an environment all evolve through state, observation, action, and feedback. TAIS turns that shared structure into models, experiments, and products.
Our first product turns natural-language questions into time-series analysis workflows, so users can work with models instead of wiring every experiment by hand.
SHUProphet is an interactive time-series Agent. Users describe an analysis task in natural language; the Agent connects the request to forecasting, anomaly analysis, visualization, and reporting modules, then explains the result in a usable workflow.
Our publication portfolio spans representation learning, forecasting, generation, uncertainty, and adaptive systems.
TAIS is designed around a clear loop: research questions, reproducible experiments, working prototypes, and feedback from real users.
Develop a defensible sequence-intelligence research contribution around Agent trajectories, evaluation, prediction, and adaptive memory, supported by open experiments and a reproducible toolkit.
Turn SHUProphet from a research prototype into a reliable analysis system with measurable workflows, privacy-conscious deployment, and clear evaluation criteria.
Ship focused prototypes in short cycles: Agent trace analysis, time-series copilots, model diagnostics, tool-use evaluation, and interactive demos that can survive real judging.
We welcome serious collaborators in time-series modeling, Agent systems, product engineering, security, design, and applied research.
Institutions represented through TAIS members, co-authored research, and our wider academic network.
TAIS brings together machine learning, product building, economics, interdisciplinary research, and security.
Our next research layer extends the same sequence-first perspective to Agent trajectories and adaptive systems.
Improve data import, forecasting comparison, anomaly diagnostics, natural-language interaction, and exportable reports for researchers and small teams.
Define a transparent format for Agent trajectories and study clustering, failure detection, state prediction, and evaluation on public or synthetic traces.
Release reproducible benchmarks, run community challenges, and offer privacy-conscious analysis or private deployment for labs and AI teams.