Paper Info
Title
An intelligent memory model for short-term prediction: an application to global solar radiation data
Abstract
This paper presents a machine learning model for short-term prediction. The proposed procedure is based on regression techniques and on the use of a special type of probabilistic finite automata. The model is built in two stages. In the first stage, the most significant independent variable is detected, then observations are classified according to the value of this variable and regressions are re-run separately for each Group. The significant independent variables in each group are then discretized. The PFA is built with all this information. In the second stage, the next value of the dependent variable is predicted using an algorithm for short term forecasting which is based on the information stored in the PFA. An empirical application for global solar radiation data is also presented. The predictive performance of the procedure is compared to that of classical dynamic regression and a substantial improvement is achieved with our procedure.
Year
DOI
Venue
2010
10.1007/978-3-642-13033-5_61
IEA/AIE (3)
Keywords
Field
DocType
significant independent variable,empirical application,classical dynamic regression,probabilistic finite automaton,global solar radiation data,intelligent memory model,next value,dependent variable,short-term prediction,proposed procedure,regression technique,predictive performance,machine learning,memory model,time series
Discretization,Regression,Computer science,Algorithm,Finite-state machine,Memory model,Artificial intelligence,Variables,Probabilistic logic,Global solar radiation
Conference
Volume
ISSN
ISBN
6098
0302-9743
3-642-13032-1
Citations 
PageRank 
References 
0
0.34
9
Authors
4
Name
Order
Citations
PageRank
Llanos Mora-Lopez111.39
Juan Mora272.28
Michel Piliougine321.11
M. Sidrach-de-Cardona4103.10