ENBIS 2021 Spring Meeting

Session

Analytical methods

2.1.1

18 May 2021, 10:00

Online

Conveners

Analytical methods

• Lluis Marco Almagro

33. Outlier detection using robust random cut forest

Dr Basiru Usman (NC State University)

18/05/2021, 10:00

Data Science in Process Industries

Due to the development of sensor devices and ubiquitous computing, we generate an enormous amount of data every second of every day. With access to a gigantic amount of information, it is imperative to analyze it, monitor it, and interpret it correctly so that business decisions are made correctly. When it comes to security, finding the anomalies is only the first step in data analysis....

49. Efficient Accounting for Estimation Uncertainty in Coherent Forecasting of Count Processes

Prof. Christian Weiß (Helmut Schmidt University)

18/05/2021, 10:20

Data Science in Process Industries

Coherent forecasting techniques for count processes generate forecasts that consist of count values themselves. In practice, forecasting always relies on a fitted model and so the obtained forecast values are affected by estimation uncertainty. Thus, they may differ from the true forecast values as they would have been obtained from the true data generating process. We propose a...

17. Bootstrapping, cross validation and SVEM: Differences and similarities with applications to industrial processes

Prof. Ron Kenett (KPA Group & Samuel Neaman Institute, Technion, Israel), Dr Chris Gotwalt (JMP Division, SAS, Research Triangle)

18/05/2021, 10:40

Data Science in Process Industries

Computer age statistics typically involves large amounts of data and application of computer intensive methods. In this talk we focus on bootstrapping, cross validation and simulation methods. We discuss their use and limitations and contrast their applications. Specifically, we show how bootstrapping used to test hypothesis is different from cross validation used to validate predictive...