A Bayesian Multiple Models Combination Method for Time Series Prediction

Authors:
V. Petridis;A. Kehagias;L. Petrou;A. Bakirtzis;S. Kiartzis;H. Panagiotou;N. Maslaris
Affiliations:
Faculty of Engineering, Aristotle University, Thessaloniki, GR 54006, Greece;Faculty of Engineering, Aristotle University, Thessaloniki, GR 54006, Greece;Faculty of Engineering, Aristotle University, Thessaloniki, GR 54006, Greece;Faculty of Engineering, Aristotle University, Thessaloniki, GR 54006, Greece;Faculty of Engineering, Aristotle University, Thessaloniki, GR 54006, Greece;Faculty of Engineering, Aristotle University, Thessaloniki, GR 54006, Greece;Greek Sugar Industry, Thessaloniki, Greece
Venue:
Journal of Intelligent and Robotic Systems
Year:
2001

Citing 9
Cited 4

Introduction to the theory of neural computation

Introduction to the theory of neural computation
Hierarchical mixtures of experts and the EM algorithm

Neural Computation
Convergence results for the EM approach to mixtures of experts architectures

Neural Networks
Predictive modular neural networks for time series classification

Neural Networks
Predictive Modular Neural Networks: Applications to Time Series

Predictive Modular Neural Networks: Applications to Time Series
Annealed competition of experts for a segmentation and classification of switching dynamics

Neural Computation
A recurrent network implementation of time series classification

Neural Computation
Predictive modular fuzzy systems for time-series classification

IEEE Transactions on Fuzzy Systems
Modular neural networks for MAP classification of time series and the partition algorithm

IEEE Transactions on Neural Networks

A comparison of global, recurrent and smoothed-piecewise neural models for Istanbul stock exchange (ISE) prediction

Pattern Recognition Letters
Discrete wavelet transform-based time series analysis and mining

ACM Computing Surveys (CSUR)
Model combination for support vector regression via regularization path

PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
Forecasting building occupancy using sensor network data

Proceedings of the 2nd International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper we present the Bayesian Combined Predictor (BCP), a probabilistically motivated predictor for time series prediction. BCP utilizes local predictors of several types (e.g., linear predictors, artificial neural network predictors, polynomial predictors etc.) and produces a final prediction which is a weighted combination of the local predictions; the weights can be interpreted as Bayesian posterior probabilities and are computed online. Two examples of the method are given, based on real world data: (a) short term load forecasting for the Greek Public Power Corporation dispatching center of the island of Crete, and (b) prediction of sugar beet yield based on data collected from the Greek Sugar Industry. In both cases, the BCP outperforms conventional predictors.