CVSL Logo
FrancaisHome
AboutPeopleResearchPublicationsEventsProfile
About
REPARTI Seminars


The REPARTI Seminars at Université Laval are held on Fridays at 11:30 a.m.
Please see the program for more details.
Dec 14 2017 1:30PM
Seminar
Deep 6-DOF Tracking
Dec 15 2017 11:00AM
Seminar
Building and Evaluating Data-Driven Neural Dialogue Systems

 

 

 

REPARTI

MIVIM

Dec 2 2011 11:30AM

Karol Lina Lopez


Selection of training examples in time series by unsupervised stratification: Application to Forecasting one-step ahead Hourly Ontario Energy Prices



Abstract

A large quantity of training data is often required in order to have enough representatives examples to ensure good performances of any learning-based forecasting method. Yet, time series composed of too many data can also be a problem. It would quite possibly take a long time to generate adequate solutions. In this presentation, a methodology based on a stratification of time series based on some clustering procedures has been developed for a prior selection of training examples. The principle for deterministically constructing folds with unsupervised stratification consists in assigning similar instances to the different folds instead of using a random sampling approach. The results show that with a small number of training examples, obtained through stratification of data, we can improve the performance and stability of models such as artificial neural network and support vector regression, while training at much lower cost. We illustrate the properties of the methodology in forecasting one-step ahead hourly Ontario energy prices (HOEP).




     
   
   

©2002-. Computer Vision and Systems Laboratory. All rights reserved