Unified knowledge based economy hybrid forecasting

Shami, Ahmad Al; Lotfi, Ahmad; Coleman, Simeon; Dostál, Petr

doi:10.1016/j.techfore.2014.01.014

Cited by 9 publications

(8 citation statements)

References 22 publications

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“…Our work largely complementary to this, since it presents a methodology that allows handling missing values and focuses on the KEI prediction for emerging and developing economies, for which being able to make predictions with incomplete information it is essential. Despite having to impute the missing data, working with more countries (71 vs. 51), and working over a much longer period (22 years vs. 5), our mean quadratic error, 0.382 + -0.065, is comparable with the one obtained by Shami (2015), 0.224.…”

Section: Resultssupporting

confidence: 48%

“…The closest work to ours is Shami (2015), where the KEI is predicted using neural networks (among other techniques, although neural networks are the ones that produced the best result) for 51 developed countries from 2007 to 2009 (five years) and without considering situations where missing values occur. The authors of this work pointed as a line of future work "to broaden the forecast to include missing data".…”

Section: Resultsmentioning

confidence: 99%

“…To achieve this objective, we shall use a state-of-the-art machine learning technique, deep learning neural networks (DLNN), to predict the KEI index for developing and emerging countries using a set of economic variables based on the World Bank Knowledge Assessment Methodology (KAM) (Chen and Dahlman, 2006). The prediction of the KEI index from economic data has been studied for developed countries, where it can be assumed that all the economic variables used in the prediction are available (Al Shami, 2015). However, data from emerging and developing countries contains a large share of missing values.…”

Section: Introductionmentioning

confidence: 99%

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Using deep learning neural networks to predict the knowledge economy index for developing and emerging economies

Andrés

Otero

Amavilah

2021

Expert Systems with Applications

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Missing values and the inconsistency of the measures of the knowledge economy remain vexing problems that hamper policy-making and future research in developing and emerging economies. This paper contributes to the new and evolving literature that seeks to advance better understanding of the importance of the knowledge economy for policy and further research in developing and emerging economies. In this paper we use a supervised machine deep learning neural network (DLNN) approach to predict the knowledge economy index of 71 developing and emerging economies during the 1995-2017 period. Applied in combination with a data imputation procedure based on the K-closest neighbor algorithm, DLNN is capable of handling missing data problems better than alternative methods. A 10-fold validation of the DLNN yielded low quadratic and absolute error (0,382 +-0,065). The results are robust and efficient, and the model's predictive power is high. There is a difference in the predictive power when we disaggregate countries in all emerging economies versus emerging Central European countries. We explain this result and leave the rest to future endeavors. Overall, this research has filled in gaps due to missing data thereby allowing for effective policy strategies. At the aggregate level development agencies, including the World Bank that originated the KEI, would benefit from our approach until substitutes come along.

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Section: Resultssupporting

confidence: 48%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Using deep learning neural networks to predict the knowledge economy index for developing and emerging economies

Andrés

Otero

Amavilah

2021

Expert Systems with Applications

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“…Based on improved Kohonen's neural networks algorithm, social media users accessing model was implemented [18], which includes two stages: learning stage and application stage (shown in Fig. 2).…”

Section: Implementation and Performance Resultsmentioning

confidence: 99%

Web mining based on one-dimensional Kohonen’s algorithm: analysis of social media websites

Liu

Zhang

Guo

2016

Neural Comput & Applic

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One-dimensional Kohonen's algorithm is a process of mining knowledge which finds the characteristics of social media websites as a mode from the sequence database. Social media web log records generated constantly, and user access patterns will change accordingly. This study focused on taking advantage of the dynamic characteristics of the Kohonen algorithm, delivering a fast and efficient incremental mining algorithm and testing the new developed model.

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“…Shami et al (2014) proposed a new framework for predicting knowledge based economy (KBE) competitiveness. They used existing KBE indicators from internationally recognised organisations to forecast and unify the KBE performance indices.…”

mentioning

confidence: 99%

Investigating the effect of value chain on knowledge application for economic development

Kangarlouei¹,

Alghyani²

2015

10.5267/j.uscm

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Unified knowledge based economy hybrid forecasting

Cited by 9 publications

References 22 publications

Using deep learning neural networks to predict the knowledge economy index for developing and emerging economies

Using deep learning neural networks to predict the knowledge economy index for developing and emerging economies

Web mining based on one-dimensional Kohonen’s algorithm: analysis of social media websites

Investigating the effect of value chain on knowledge application for economic development

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