Macroeconomic Time Series Prediction Using
Prediction Networks And Evolutionary
Algorithms
Author(s)
P. Forsberg & M. Wahde
Abstract
The prediction of macroeconomic time series by means of a form of fully recurrent
neural networks, called discrete-time prediction networks (DTPNs), is considered.
The DTPNs are generated using an evolutionary algorithm, allowing both
structural and parametric modifications of the networks, as well as modifications
in the squashing function of individual neurons.
The results show that the evolved DTPNs achieve better performance on
both training and validation data compared to benchmark prediction methods.
The importance of allowing structural modifications in the evolving networks is
discussed. Finally, a brief investigation of predictability measures is presented.
Keywords: time series prediction, recurrent neural networks, evolutionary
algorithms.
1 Introduction
Prediction of time series is an important problem in many fields, including
economics. Due to the high level of noise in macroeconomic time series, models
involving two parts, one deterministic and one stochastic, are often used. One
such method is ARIMA [1]. For one-step prediction, the results obtained by these
simple predictive methods (such as exponential smoothing, which is a special
case of ARIMA models), are difficult to improve much due to the high levels of
noise present. However, even a small improvement can translate into considerable
amounts of money for data sets that concern e.g. an entire national economy.
The aims of this paper is (1) to introduce a class of generalized, recurrent neural
Keywords
time series prediction, recurrent neural networks, evolutionary
algorithms.
Related Book
Other papers in this volume
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