Scalable AutoML for Time Series Forecasting using Ray

Time Series Forecasting is widely used in real world applications, such as network quality analysis in Telcos, log analysis for data center operations, predictive maintenance for high-value equipment, and etc. Recently there's a trend to apply machine learning and deep learning methods to such problems, and there's evidence that they can outperform traditional methods (such as autoregression and exponential smoothing) in several well-known competitions and real-world use cases.

However, building the machine learning applications for time series forecasting can be a laborious and knowledge-intensive process. In order to provide an easy-to-use time series forecasting toolkit, we have applied Automated Machine Learning (AutoML) to time series forecasting. The toolkit is built on top of Ray (a distributed framework for emerging AI applications open-sourced by UC Berkeley RISELab), so as to automate the process of feature generation and selection, model selection and hyper-parameter tuning in a distributed fashion. In this talk we will share how we build the AutoML toolkit for time series forecasting, as well as real-world experience and take aways from earlier users.