||Comparing heuristic approaches for the prediction of noisy time series
||Boss, Niklas Skamriis
||Witt, Carsten (Algorithms and Logic, Department of Informatics and Mathematical Modeling, Technical University of Denmark, DTU, DK-2800 Kgs. Lyngby, Denmark)
||Technical University of Denmark, DTU, DK-2800 Kgs. Lyngby, Denmark
||The typical study of the performance of heuristics for time series predictions,
tests only a single approach. This thesis describes the work on three heuristics
for time series predictions: Genetic Programming, Neural Networks and Particle
Swarm Optimization. The aim is to investigate how the performance of the
heuristics degrades as noise in the input data increases, in both theoretical time
series and time series of real scenarios.
A thorough description of the heuristics is given, to give an insight in the important mechanisms of the heuristics. This sets the ground for the choices during
the experiments, and provides the knowledge to support the results and observations from the experiments.
The objective of the practical work is to implement a framework for creating
multiple investigations on how random noise affects the predictive capabilities
of the heuristics. The noise will be added to the input data to increase the
difficulty of the prediction tasks.
The quality of the predictions will be compared across three time series using
the Mean Square Error. The results will form the base for a conclusion of some
general tendencies, as well as recommendations for future work.
||Technical University of Denmark (DTU) : Kgs. Lyngby, Denmark
Creation date: 2011-01-19
Update date: 2011-01-19