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Loughborough University Institutional Repository Unified knowledge based economy hybrid forecasting
| Content Provider | Semantic Scholar |
|---|---|
| Author | Shami, Ahmad Al Lotfi, Ahmad Coleman, Simeon Dostál, Petr |
| Copyright Year | 2018 |
| Abstract | In this paper, a new framework for forecasting Knowledge Based Economy (KBE) competitiveness is proposed. Existing KBE indicators from internationally recognised organisations including the World Bank and the International Telecommunication Union among others are used to forecast and unify the KBE performance indices. Three different forecasting methods including Panel Data: time-series cross sectional (TSCS), Linear Multiple Regression (LMREG), and Artificial Neural Network (ANN) are investigated. The ANN forecasting model outperformed the TSCS and LMREG. The proposed KBE forecasting model utilizes a 2-stage ANN model which are fed with panel data set structure. The first stage of the model consists of a feed-forward neural network that feeds to a Kohonen’s Self-Organizing Map (SOM) in the second stage of the model. Feed-forward neural network is used to learn and predict the scores of nations using past observed data. Then, SOM is used to aggregate the forecasted scores and to place nations in homogeneous clusters. The proposed framework can be applied in the context of forecasting the competitiveness of KBE for any nation even over small time periods or missing data. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://dspace.lboro.ac.uk/dspace-jspui/bitstream/2134/22865/3/TFSC_v1.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
