Reservoir computing in financial forecasting with committee methods

Konrad Stanek

AbstractReservoir Computing (RC) methods are an active area of research in the field of machine learning and intelligent processing. In particular, reservoir networks (echo-state networks, ESN) have been successfully applied to many engineering problems such as chaotic time series forecasting, primarily due to their efficiency, speed of training, and avoidance of many common shortcomings of typical re-current neural networks. The initial concept of echo state networks became soon extended with such techniques as supervised/unsupervised reservoir adaptation, weights pruning and feature selection, improved training algorithms. Simultaneously, other research efforts concentrated on combining individual networks into hierarchical structures or voting collectives. In this work we follow this concept and evaluate various types of ESN committees. Furthermore, we investigate different member ranking algorithms and show circumstances in which they constitute promising alternative to simple output averaging. The results of our comparative studies suggest several design principles concerning committee models.
Secondly, we shall apply the reservoir committee models to non-trivial engineering task of financial forecasting. The global markets constitute one of the most complex, non-linear systems created by modern society. For decades it was a goal of many research endeavors to understand and foresee the essential mechanisms of markets dynamics. While for many contributors the ability to forecast the chaotic financial time series is the purpose in itself, for others, like banks, investment funds, or governmental entities, application of steadily better models is the integral part of the investment strategy and decision taking processes. Multitude of various approaches are intensively investigated in light of their applicability to financial forecasting, however it still remains uncertain if any of the proposed models can clearly outperform the others in this task. In the scope of this thesis we employ the ESN committee models to forecast the probable market movements. We shall consider a range of optimization schemes and training configurations. Important part of the thesis will relate to domain analysis in order to facilitate selection and preprocessing of the input data, so that optimal amount of information is provided to the system.
TypeMaster's thesis [Academic thesis]
PublisherTechnical University of Denmark, DTU Informatics, E-mail:
AddressAsmussens Alle, Building 305, DK-2800 Kgs. Lyngby, Denmark
NoteSupervised by Associate Professor Ole Winther, DTU Informatics
Electronic version(s)[pdf]
Publication link
BibTeX data [bibtex]
IMM Group(s)Intelligent Signal Processing