Scalable AutoML for Time Series Forecasting using Ray

Shengsheng Huang and Jason Dai, Intel

Abstract: 

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.

Jason Dai, Intel

Jason Dai is a senior principal engineer and CTO of Big Data Technologies at Intel, responsible for leading the global engineering teams (in both Silicon Valley and Shanghai) on the development of advanced data analytics and machine learning. He is the creator of BigDL and Analytics Zoo, a founding committer and PMC member of Apache Spark, and a mentor of Apache MXNet. For more details, please see https://jason-dai.github.io/.

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BibTeX
@conference {256672,
author = {Shengsheng Huang and Jason Dai},
title = {Scalable AutoML for Time Series Forecasting using Ray},
year = {2020},
publisher = {{USENIX} Association},
month = jul,
}

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