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Using Artificial Intelligence Techniques for Economic Time Series Prediction

Contemporary Issues in Behavioral Finance

ISBN: 978-1-78769-882-6, eISBN: 978-1-78769-881-9

ISSN: 1569-3759

Publication date: 4 July 2019

Abstract

In this chapter the authors will focus on employing Machine Learning techniques for predicting data for future states of economic using techniques which include Artificial Neural Networks, Adaptive Neuro-Fuzzy Inference System, Dynamic Boltzmann Machine, Support Vector Machine, Hidden Markov Model, Bayesian Learning on Gaussian process model, Autoregressive Integrated Moving Average, Autoregressive Model (Poggi, Muselli, Notton, Cristofari, & Louche, 2003), and K-Nearest Neighbor Algorithm. Findings revealed positive results in terms of predicting economic data.

Keywords

Citation

Kose, U. (2019), "Using Artificial Intelligence Techniques for Economic Time Series Prediction", Grima, S., Özen, E., Boz, H., Spiteri, J. and Thalassinos, E. (Ed.) Contemporary Issues in Behavioral Finance (Contemporary Studies in Economic and Financial Analysis, Vol. 101), Emerald Publishing Limited, Bingley, pp. 13-28. https://doi.org/10.1108/S1569-375920190000101002

Publisher

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Emerald Publishing Limited

Copyright © 2019 Emerald Publishing Limited