ML Seminar Series

The easiest way to improve time series forecasting?

by Mr Carl Remlinger

Europe/Zurich
OHSA/E13 (https://psich.zoom.us/j/64271153417?pwd=eFFqRDY4UmZhYUh3SFlRa1dGanJ5UT09 Meeting-ID: 642 7115 3417 Kenncode: 502508 )

OHSA/E13

https://psich.zoom.us/j/64271153417?pwd=eFFqRDY4UmZhYUh3SFlRa1dGanJ5UT09 Meeting-ID: 642 7115 3417 Kenncode: 502508

Description

Abstract: 
Machine learning algorithms dedicated to time series forecasting have gained a lot of interest over the last few years. One difficulty lies in the choice between several algorithms, as their estimation accuracy may be unstable over time.  Online Aggregation of Experts combines a finite set of forecasting models without making assumptions about the data. The mixture is updated continuously when data becomes available. This is a desirable feature in non-stationary environments as it allows to reconsider at each time step what are the best estimators. 
 

REGISTRATION DEADLINE ==> MO 13/03/23, 12 am

 

Organised by

SDSC hub at PSI

Registration
Participants
Participants
  • Andrej Babič
  • Arnau Albà
  • Bastien Golomer
  • Benjamín Béjar Haro
  • Cecilia Casadei
  • Dmitry Sotnikov
  • Jochem Snuverink
  • Louis Berry
  • Mahdieh Shakoorioskooie
  • Piero Gasparotto
  • Qianru Zhan
  • Raphael Husistein
  • Renato Bellotti
  • Spencer Bliven
  • Xiangyu Xie
  • +15
Dr. B. Bejar Haro