Bringing Time-Series Forecasting into Apache IoTDB Database

A Covariate Forecasting Framework with TimechoDB AINode In industrial time-series forecasting scenarios, accurate trend prediction often serves as a critical foundation for operational decision-making. However, traditional univariate forecasting approaches struggle to fully capture the complexity of real-world systems. Take electricity pricing as an example. Power prices are not determined solely by historical price patterns. They are also influenced by a variety of external factors, including temperature, wind speed, holidays, and energy supply structure. Similar multivariate dependencies exist across many industries, such as manufacturing, transportation, and energy systems. As the scale and complexity of time-series data continue to grow, forecasting is no longer purely an algorithmic problem. Increasingly, it requires tight integration between data infrastructure and model capabilities. In TimechoDB, we significantly enhanced the capabilities of AINode, the database's intelligent a