Using Artificial Intelligence Techniques for Economic Time Series Prediction

Abstract

It is possible to see effective use of Artificial Intelligence-based systems in many fields because it easily outperforms traditional solutions or provides solutions for the problems not previously solved. Prediction applications are a widely used mechanism in research because they allow for forecasting of future states. Logical inference mechanisms in the field of Artificial Intelligence allow for faster and more accurate and powerful computation. Machine Learning, which is a sub-field of Artificial Intelligence, has been used as a tool for creating effective solutions for prediction problems.

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.

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, Leeds, pp. 13-28. https://doi.org/10.1108/S1569-375920190000101002