Learning probabilistic automata with variable memory length
COLT '94 Proceedings of the seventh annual conference on Computational learning theory
On the learnability and usage of acyclic probabilistic finite automata
Journal of Computer and System Sciences - Special issue on the eighth annual workshop on computational learning theory, July 5–8, 1995
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Discretization: An Enabling Technique
Data Mining and Knowledge Discovery
Multi-interval Discretization Methods for Decision Tree Learning
SSPR '98/SPR '98 Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Probabilistic Finite-State Machines-Part I
IEEE Transactions on Pattern Analysis and Machine Intelligence
PAC-learnability of probabilistic deterministic finite state automata in terms of variation distance
Theoretical Computer Science
Expert Systems with Applications: An International Journal
Hi-index | 0.00 |
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.