| Title |
Prediction of composite indicators using combined method of extreme learning machine and locally weighted regression / |
| Authors |
Rukšėnaitė, Jurga ; Vaitkus, Pranas |
| DOI |
10.15388/NA.17.2.14071 |
| Full Text |
|
| Is Part of |
Nonlinear analysis: modelling and control.. Vilnius : Vilniaus universiteto leidykla. 2012, vol. 17, no.2. ISSN 1392-5113. eISSN 2335-8963 |
| Keywords [eng] |
composite indicators ; neural networks ; ELM ; locally weighted regression |
| Abstract [eng] |
In this paper, a method of artificial neural networks (NN) is proposed as an alternative tool for the one-step-ahead prediction of composite indicators (CIs) of Lithuania’s economy. CI is composed of widely used social and economic indicators. The NN is applied for forecasting CI during the financial crisis and later periods (2008–2010) on the basis of data of earlier years (1998– 2007). In this work, the Extreme Learning Machine (ELM) algorithm is combined with locally weighted regression. The analysis shows that the prediction error of a testing sample is statistically smaller compared to Levenberg–Marquardt or ELM methods. |
| Published |
Vilnius : Vilniaus universiteto leidykla |
| Type |
Journal article |
| Language |
English |
| Publication date |
2012 |
| CC license |
|
