一句话：提出 TTSF-transformer 可迁移深度 Transformer 预测服务，用于在线系统时间序列指标。
在线系统希望预测软件质量的未来趋势，以自动化运维、优化资源成本并保障可靠性。本文提出 TTSF-transformer，一种可迁移的时间序列预测服务。该方法先对多源指标频率进行归一化，使模型可在异构系统间共享；采用带贝叶斯估计的深度 Transformer 生成预测边际分布；并将迁移学习与增量学习引入训练过程，以提升长期预测性能。作者在腾讯两类游戏业务的真实时间序列指标上评估，结果表明 TTSF-transformer 显著优于现有最先进方法，适合在大型在线系统中广泛部署。
Many real-world online systems expect to forecast the future trend of software quality to better automate operational processes, optimize software resource cost and ensure software reliability. To achieve that, all kinds of time series metrics collected from online software systems are adopted to characterize and monitor the quality of software services. To meet relevant software engineers’ requirements, we focus on time series forecasting and aim to provide an event-driven and self-adaptive forecasting service. In this paper, we present 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 performance of long-term prediction. 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.
@inproceedings{paperbot7355,
title = {A Transferable Time Series Forecasting Service Using Deep Transformer Model for Online Systems},
author = {Tao Huang and Pengfei Chen and Jingrun Zhang and Ruipeng Li and Rui Wang},
booktitle = {ASE},
year = {2022},
doi = {10.1145/3551349.3560414}
}