報告題目：orecasting Big Time Series: Theory and Practice
報告時間：2020.04
報告介紹：https://lovvge.github.io/Forecasting-Tutorial-WWW-2020/
報告錄影：https://www.amazon.science/videos-and-tutorials/forecasting-big-time-series-theory-and-practice

報告專家：
Christos Faloutsos (CMU and Amazon)
Valentin Flunkert (AWS AI Labs)
Jan Gasthaus (AWS AI Labs)
Tim Januschowski (AWS AI Labs)
Yuyang (Bernie) Wang (AWS AI Labs)

報告簡介


Time series forecasting is a key ingredient in the automation and optimization of business processes: in retail, deciding which products to order and where to store them depends on the forecasts of future demand in different regions; in cloud computing, the estimated future usage of services and infrastructure components guides capacity planning; and workforce scheduling in warehouses and factories requires forecasts of the future workload. Recent years have witnessed a paradigm shift in forecasting techniques and applications, from computer-assisted model- and assumption-based to data-driven and fully-automated. This shift can be attributed to the availability of large, rich, and diverse time series data sources and result in a set of challenges that need to be addressed such as the following. How can we build statistical models to efficiently and effectively learn to forecast from large and diverse data sources? How can we leverage the statistical power of “similar” time series to improve forecasts in the case of limited observations? What are the implications for building forecasting systems that can handle large data volumes?	時間序列預測是業務流程自動化和優化的關鍵要素：在零售中，決定要訂購哪些產品以及將它們存儲在何處取決於對不同地區未來需求的預測。在雲計算中，服務和基礎架構組件的未來估計使用量將指導容量規劃；倉庫和工廠中的人員調度需要對未來的工作量進行預測。近年來，預測技術和應用已發生了範式轉變，從基於計算機輔助的模型和假設的預測，到以數據爲驅動力的全自動化模型。這種轉變可以歸因於大量，豐富和多樣的時間序列數據源的可用性，並導致一系列需要解決的挑戰，例如以下。我們如何建立統計模型，以有效地學習來自大量不同數據源的預測？在觀測值有限的情況下，我們如何利用“相似”時間序列的統計能力來改善預測？構建可處理大數據量的預測系統有什麼含義？
The objective of this tutorial is to provide a concise and intuitive overview of the most important methods and tools available for solving large-scale forecasting problems. We review the state of the art in both: (1) classical modeling of time series, (2) deep learning for forecasting. We also discuss the practical aspects of building a large scale forecasting system, including data integration, feature generation, backtesting framework, error tracking and analysis, etc. Accompanied with the practice side is a hands-on session, where we would engage the audience with Jupyter notebooks that demonstrates the key concepts in the theory part. Furthermore, we provides interactive demos, showing various avenues to solve business problems with AWS Forecasting offerings such as GluonTS, DeepAR (SageMaker), and Amazon Forecast.	本教程的目的是簡要直觀地概述可用於解決大規模預測問題的最重要方法和工具。我們在以下兩個方面都審查了最新技術：（1）時間序列的經典建模，（2）預測的深度學習。我們還討論了構建大型預測系統的實際方面，包括數據集成，功能生成，回測框架，錯誤跟蹤和分析等。與實踐相關的是一次動手實踐會議，我們將與觀衆互動Jupyter筆記本在理論部分演示了關鍵概念。此外，我們提供了交互式演示，展示了使用AWS Forecasting產品（例如GluonTS，DeepAR（SageMaker）和Amazon Forecast）解決業務問題的各種途徑。
Keywords: Forecasting, Neural Network, Time Series	關鍵字：預測，神經網絡，時間序列

內容梗概

Part 1 – Fundamentals

P1.1. Similarity search: Euclidean/time-warping; feature extraction and SAMs
P1.2. Periodicities: DFT/DWT
P1.3. Linear Forecasting: AR (Box-Jenkins)
P1.4 Non-linear forecasting: lag-plots– Gray-box modeling: Lotka-Volterra
P1.5. Tensors: PARAFAC

Motivation - Applications

Financial, sales, economic series
Medical
– reactions to new drugs
– elderly care
civil/automobile infrastructure
– bridge vibrations [Oppenheim+02]
– road conditions / traffic monitoring
Weather, environment/anti-pollution
– volcano monitoring
– air/water pollutant monitoring
– sunspots
Computer systems
– web servers (caching, prefetching)
– network traffic monitoring
– …