Shengsheng Huang

Scalable AutoML for Time Series Forecasting using RaySummit 2020

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, etc. Classical time series forecasting methods (such as autoregression and exponential smoothing) often involve making assumptions the underlying distribution of the data, while new machine learning methods, especially neural networks often perceive time series forecasting as a sequence modeling problem and have recently been applied to these problems with success (e.g., [1] and [2]). 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 'war stories' of earlier users (such as Tencent). References:

1. Guokun Lai, Wei-Cheng Chang, Yiming Yang, Hanxiao Liu. 'Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks'
2. Nikolay Laptev, Slawek Smyl, Santhosh Shanmugam. 'Engineering Extreme Event Forecasting at Uber with Recurrent Neural Networks'