tspDB：时间序列预测DB
tspDB: Time Series Predict DB
摘要：
当前机器学习（ML）工作流程的一个主要瓶颈是从数据存储或数据库（DB）获取数据到ML算法可以应用于它的点所需的耗时，易出错的工程。因此，我们探索了在数据存储或DB之上直接集成预测功能的可行性。这样的系统理想地：（i）提供直观的预测查询界面，简化了笨拙的数据工程;（ii）提供最先进的统计准确性，同时确保增量模型更新，低模型训练时间和低预测延迟。作为主要贡献，我们显式地实例化了一个概念验证，tspDB，它直接与PostgreSQL集成。我们严格测试了tspDB的统计和计算性能，并使用了最先进的时间序列算法，包括长短期记忆（LSTM）神经网络和DeepAR（Amazon的行业标准深度学习库）。从统计学上看，在标准时间序列基准测试中，tspDB的相对准确度比LSTM和DeepAR高1.1- 1.3倍。在计算方面，tspDB在中值ML模型训练时间和预测查询延迟方面分别比LSTM和DeepAR快59- 62倍和94- 95倍。此外，与PostgreSQL的批量插入时间和SELECT查询延迟相比，tspDB仅分别慢1.3倍和2.6倍。也就是说，tspDB是一个实时预测系统，因为它的模型训练/预测查询时间类似于从DB插入/读取数据。作为算法的贡献，我们介绍了一个增量的多元矩阵分解为基础的时间序列方法，其中tspDB是建立了。我们表明，这种方法也可以通过准确估计时间序列的时变方差来产生可靠的预测区间，从而解决时间序列分析中的一个重要问题。
英文摘要：
A major bottleneck of the current Machine Learning (ML) workflow is the time consuming, error prone engineering required to get data from a datastore or a database (DB) to the point an ML algorithm can be applied to it. Hence, we explore the feasibility of directly integrating prediction functionality on top of a data store or DB. Such a system ideally: (i) provides an intuitive prediction query interface which alleviates the unwieldy data engineering; (ii) provides state-of-the-art statistical accuracy while ensuring incremental model update, low model training time and low latency for making predictions. As the main contribution we explicitly instantiate a proof-of-concept, tspDB, which directly integrates with PostgreSQL. We rigorously test tspDB's statistical and computational performance against the state-of-the-art time series algorithms, including a Long-Short-Term-Memory (LSTM) neural network and DeepAR (industry standard deep learning library by Amazon). Statistically, on standard time series benchmarks, tspDB outperforms LSTM and DeepAR with 1.1-1.3x higher relative accuracy. Computationally, tspDB is 59-62x and 94-95x faster compared to LSTM and DeepAR in terms of median ML model training time and prediction query latency, respectively. Further, compared to PostgreSQL's bulk insert time and its SELECT query latency, tspDB is slower only by 1.3x and 2.6x respectively. That is, tspDB is a real-time prediction system in that its model training / prediction query time is similar to just inserting / reading data from a DB. As an algorithmic contribution, we introduce an incremental multivariate matrix factorization based time series method, which tspDB is built off. We show this method also allows one to produce reliable prediction intervals by accurately estimating the time-varying variance of a time series, thereby addressing an important problem in time series analysis.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
