Forecasting US Unemployment with Radial Basis Neural Networks, Kalman Filters and Support Vector Regressions

Stasinakis, Charalampos; Theofilatos, Konstantinos; Karathanasopoulos, Andreas

doi:10.1007/s10614-014-9479-y

Cited by 27 publications

(9 citation statements)

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“…Kao (2001, 2002) apply support vector machines in financial time series forecasting. Stasinakis et al (2015) use a radial basis function ANN to forecast US unemployment. Feng and Zhang (2014) and Aminian et al (2006) use ANN models in forecasting of economic growth.…”

Section: Introductionmentioning

confidence: 99%

Modelling cross-dependencies between Spain’s regional tourism markets with an extension of the Gaussian process regression model

Clavería

Monte

Porras

2016

SERIEs

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This study presents an extension of the Gaussian process regression model for multiple-input multiple-output forecasting. This approach allows modelling the cross-dependencies between a given set of input variables and generating a vectorial prediction. Making use of the existing correlations in international tourism demand to all seventeen regions of Spain, the performance of the proposed model is assessed in a multiple-step-ahead forecasting comparison. The results of the experiment in a multivariate setting show that the Gaussian process regression model significantly improves the forecasting accuracy of a multi-layer perceptron neural network used as a benchmark. The results reveal that incorporating the connections between different markets in the modelling process may prove very useful to refine predictions at a regional level.

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Section: Introductionmentioning

confidence: 99%

Modelling cross-dependencies between Spain’s regional tourism markets with an extension of the Gaussian process regression model

Clavería

Monte

Porras

2016

SERIEs

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“…Similarly, Kreiner and Duca (2019) use artificial neural networks (ANN) to forecast the U.S. unemployment outperforming the SPF benchmark results. Stasinakis et al (2014) forecast the U.S. unemployment rate using radial basis function neural networks (RBFNN), Kalman filters, and support vector regression. The models are tested against an ARMA, a STAR, and three different ΑNN.…”

Section: Introductionmentioning

confidence: 99%

Forecasting unemployment in the euro area with machine learning

Gogas

Papadimitriou

Sofianos

2021

Journal of Forecasting

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Unemployment has a direct impact on public finances and yields serious sociopolitical implications. This study aims to directionally forecast the euro-area unemployment rate. To the best of our knowledge, no other studies forecast the euro-area unemployment rate as a whole. The data set includes the unemployment rate and 36 explanatory variables, as suggested by theory and the relevant literature, spanning the period from 1998:4 to 2019:9 in monthly frequency. These variables are fed to three machine learning methodologies: decision trees (DT), random forests (RF), and support vector machines (SVM), while an elastic-net logistic regression (logit) model is used from the area of econometrics. The results show that the optimal RF model outperforms the other models by reaching a full-dataset forecasting accuracy of 88.5% and 85.4% on the out-of-sample.

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“…Modern science offers a variety of approaches and methods to the study and forecasting of unemployment using temporal (Voineagu, Pisica, & Caragea, 2012;Dumičić, Žmuk, & Časni, 2017;Stasinakis, Sermpinis, Theofilatos, & Karathanasopoulos, 2016) or spatial (Bakanach & Proskurina, 2016;Brozek & Kogut, 2016) data. This paper presents an econometric study of the unemployment rate both in time (analysis of dynamics, forecast) and in space (study of regional unemployment, its differentiation, identification of factors that determine the unemployment rate in the regions of the Russian Federation).…”

Section: Introductionmentioning

confidence: 99%

Regional Differentiation Of Unemployment Rate In The Russian Federation: Econometric Analysis

Shirnaeva¹,

Sukhanova²,

Repina³

2020

European Proceedings of Social and Behavioural Sciences

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Forecasting US Unemployment with Radial Basis Neural Networks, Kalman Filters and Support Vector Regressions

Cited by 27 publications

References 50 publications

Modelling cross-dependencies between Spain’s regional tourism markets with an extension of the Gaussian process regression model

Modelling cross-dependencies between Spain’s regional tourism markets with an extension of the Gaussian process regression model

Forecasting unemployment in the euro area with machine learning

Regional Differentiation Of Unemployment Rate In The Russian Federation: Econometric Analysis

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