Synchronize AI and DevOps

B uilding management company is forecasting energy and resource consumption in a smart building. It can use AutoAI Time Series to quickly train models with less effort than conventional approaches. With just a few mouse clicks, AutoAI Time Series can quickly identify the best features, transforms and models, as well as train the models, tune the hyperparameters and rank the best-performing pipelines — specifically for the company’s resource consumption data.

Combing through the data

While some approaches perform automated tuning and selection of models, these approaches often focus on one class of models, such as autoregressive approaches, which learn from past values of a time series in order to predict future values. Our AutoAI Time Series system is different. It achieves leading benchmark performance and accuracy across a variety of univariate datasets — be it social networks such as Twitter, phone call data logs, the weather, travel times and production volumes. It even works with multivariate datasets, such as exchange rates, household energy use, retail sales, and traffic data. Figures 2 and 3 show the performance results of AutoAI Time Series and several state of the art (SOTA) toolkits on more than 62 univariate and more than 9 multivariate data sets from a variety of application domains and ranging in size from dozens to more than 1,400,000 samples.