Kalman filter and SVR combinations in forecasting US unemployment

Sermpinis, G., Stasinakis, C. and Karathanasopoulos, A. (2013) Kalman filter and SVR combinations in forecasting US unemployment. Artificial Intelligence Applications and Innovations, 412, pp. 506-515. (doi:10.1007/978-3-642-41142-7_51)

Full text not currently available from Enlighten.

Abstract

The motivation for this paper is to investigate the efficiency of a Neural Network (NN) architecture, the Psi Sigma Network (PSN), in forecasting US unemployment and compare the utility of Kalman Filter and Support Vector Regression (SVR) in combining NN forecasts. An Autoregressive Moving Average model (ARMA) and two different NN architectures, a Multi-Layer Perceptron (MLP) and a Recurrent Network (RNN), are used as benchmarks. The statistical performance of our models is estimated throughout the period of 1972-2012, using the last seven years for out-of-sample testing. The results show that the PSN statistically outperforms all models’ individual performances. Both forecast combination approaches improve the statistical accuracy, but SVR outperforms substantially the Kalman Filter.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Stasinakis, Dr Charalampos and Sermpinis, Professor Georgios
Authors: Sermpinis, G., Stasinakis, C., and Karathanasopoulos, A.
College/School:College of Social Sciences > Adam Smith Business School > Economics
Journal Name:Artificial Intelligence Applications and Innovations
ISSN:1868-4238

University Staff: Request a correction | Enlighten Editors: Update this record