.png){kind=icon}
返回
Time Series Forecasting for Self-Aware Systems
.png){kind=icon}
PRE
AI
DOI：10.1109/JPROC.2020.2983857.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Modern distributed systems and Internet-of-Things applications are governed by fast living and changing requirements. Moreover, they have to struggle with huge amounts of data that they create or have to process. To improve the self-awareness of such systems and enable proactive and autonomous decisions, reliable time series forecasting methods are required. However, selecting a suitable forecasting method for a given scenario is a challenging task. According to the No-Free-Lunch Theorem, there is no general forecasting method that always performs best. Thus, manual feature engineering remains to be a mandatory expert task to avoid trial and error. Furthermore, determining the expected time-to-result of existing forecasting methods is a challenge. In this article, we extensively assess the state-of-the-art in time series forecasting. We compare existing methods and discuss the issues that have to be addressed to enable their use in a self-aware computing context. To address these issues, we present a step-by-step approach to fully automate the feature engineering and forecasting process. Then, following the principles from benchmarking, we establish a level-playing field for evaluating the accuracy and time-to-result of automated forecasting methods for a broad set of application scenarios. We provide results of a benchmarking competition to guide in selecting and appropriately using existing forecasting methods for a given self-aware computing context. Finally, we present a case study in the area of self-aware data-center resource management to exemplify the benefits of fully automated learning and reasoning processes on time series data.
Keyword:
Forecasting
Time series analysis
Cognition
Task analysis
Casting
Predictive models
Buildings
Feature engineering
forecasting competition
self-aware computing
time series analysis
time series forecasting
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
25.9
论文数:
9.9K
被引数:
4.5W
机构
引用论文
Meta-learning for time series forecasting and forecast combination时间序列预测和预测组合的元学习.png)
NEUROCOMPUTING
OAAI
NEUROCOMPUTINGIF6.5
OAAI分享收藏Water and Sanitation in Developing Countries: Geochemical Aspects of Quality and TreatmentElements
OAAI
ElementsIF0
OAAI分享收藏分享收藏
查看更多内容