Many real-world online systems require the forecast of monitored time series metrics to detect and localize anomalies, schedule resources, and assist relevant staffs in decision making. Even though many time series forecasting techniques have been proposed, few of them can be directly applied in online systems due to their efficiency and lack of model sharing. To address the challenges, this paper presents TTSF-transformer, a transferable time series forecasting service using deep transformer model. TTSF-transformer normalizes multiple metric frequencies to ensure the model sharing across multi-source systems, employs a deep transformer model with Bayesian estimation to generate the predictive marginal distribution, and introduces transfer learning and incremental learning into the training process to ensure the long-term performance. We conduct experiments on real-world time series metrics from two different types of game business in Tencent. The results show that TTSF-transformer significantly outperforms other state-of-the-art methods and is suitable for wide deployment in large online systems.

Tao Huang

Tencent

Pengfei Chen

Sun Yat-Sen University

Jingrun Zhang

School of Data and Computer Science, Sun Yat-sen University

Ruipeng Li

Tencent

Rui Wang

Tencent