Economic growth forecasting by artificial neural network with extreme learning machine based on trade, import and export parameters

Sokolov-Mladenovi, Svetlana; Milovanevi, Milos; Mladenovi, Igor; Alizamir, Meysam

doi:10.1016/j.chb.2016.08.014

Cited by 68 publications

(26 citation statements)

References 17 publications

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“…A comparison was drawn between the ELM algorithm and classical machine learning algorithms in the academic literature, after which the superiority of the ELM algorithm was again verified [46,47]. Therefore, ELM has been widely applied to prediction studies across multiple fields with excellent results [48,49]. Several prediction algorithms, including NN, SVR, and ELM, are introduced in this paper, while the time series of inbound tourist flows and officially issued statistical yearbook tourism data following normalization (see Section 2.3) are adopted as learning data for the prediction model.…”

Section: Prediction Modelmentioning

confidence: 98%

Geo-Tagged Photo Metadata Processing Method for Beijing Inbound Tourism Flow

Wen

Zheng

et al. 2019

IJGI

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Technological advances have led to numerous developments in data sources. Geo-tagged photo metadata has provided a new source of mass research data for tourism studies. A series of data processing methods centering on the various types of information contained in geo-tagged photo metadata have thus been proposed; as a result, the development of tourism studies based on such data has advanced. However, an in-depth study of the data processing methods designed to conduct tourist flow prediction based on geo-tagged photo metadata has not yet been conducted. In order to acquire accurate substitutive data regarding inbound flows in cities, this paper introduces and designs several methods, including data screening, text data similarity calculation, geographical location clustering, and time series data modelling, in order to realize a data preprocessing model for inbound tourist flows in cities based on geo-tagged photo metadata. Wherein, the entropy filtering method was introduced to aid in determining whether the data were posted by inbound tourists; whether the inbound persons’ activities were related to tourism was judged through the calculation of tag text similarity; an efficient clustering method based on geographic grid partition was designed for cases in which the tag values were empty; finally, the time series of the inbound tourist flows of a certain region and period were obtained through data statistics and normalization. For the empirical research, Beijing City in China was selected as the research case, after which the feasibility and accuracy of the methods proposed in this paper were verified through data correlation analysis between Flickr data and real statistical yearbook data, as well as analysis of the prediction results based on a machine learning algorithm. The data preprocessing method introduced and designed in this paper provides a reference for the study of geo-tagged photo metadata in the field of tourism flow prediction. These methods can effectively filter out inbound tourist flow data from geotag photo metadata, thus providing a novel, reliable, and low-cost research data source for urban inbound tourism flow forecasting.

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Section: Prediction Modelmentioning

confidence: 98%

Geo-Tagged Photo Metadata Processing Method for Beijing Inbound Tourism Flow

Wen

Zheng

et al. 2019

IJGI

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“…Gravity and endogenous growth models also contributed to the theoretical advances along with the theory of competitive advantage. Examples of empirical foreign trade studies, relevant to the presented work, include Ersen et al (2019), Kitworawut and Rungreunganun (2019), Sokolov-Mladenovic et al (2016), Kankal and Uzlu (2017), and Anderton, Baltagi, Skudelni, and Sousa (2005) among others. The authors of the first study model the Canadian foreign trade flows as a function of the agents' decisions in solving a dynamic optimization problem.…”

Section: Theoretical Grounds With Empirical Studiesmentioning

confidence: 99%

“…Trade deficit plays a significant part in the formation of both fiscal and monetary policies of a country, export is a vibrant constituent of the GDP of any country, and export has a direct influence on the balance of payment, the creation of employment, and economic growth. Several factors exhibit the export performance of any country; these factors are divided into supply and demand relatedfactors (Sokolov-Mladenovic, Milovancevic, Mladenovic, & Alizamir, 2016;Ersen, Akyuz, & Bayram, 2019). The capacity of the trading partners such as gross domestic product (GDP), exportable prices, competitor's prices, exchange rate, policy matters, and political stability are the demand-side factors.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Exports and Imports by using Autoregressive (AR) with Seasonal Dummies and Box-Jenkins Approaches: A Case of Pakistan

Ghauri

Ahmed

Štreimikienė

et al. 2020

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This research aims to evaluate two econometric models to forecast imports and exports for the financial year (FY) 2020. For this purpose, we used the annual exports and imports data of Pakistan from FY2002 to FY2019. Thus, in this regard, we employed, and compared the results of two econometrics models such as Box Jenkins or Autoregressive Integrated Moving Average (ARIMA), and Auto-Regressive (AR) with seasonal dummies. For examining the precision of forecasting, we employed mean absolute error and root mean square error approaches. The findings of Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) reveal that the ARIMA or Box Jenkins approach provides better accuracy of the forecast for the exports as compared to the AR model with dummies. However, Auto-Regressive (AR) model has demonstrated more precision for the imports as compared to the Box Jenkins model. Hence, the projected forecasting for the growth of export is 1.87% for the FY2020 and projected forecasting for the import demonstrates a negative variation of -1.61% for the FY2020. The findings of the undertaken study recommend the policymakers of Pakistan to take corrective measures to increase exports and to prevent the country from the trade deficit. The policymakers of Pakistan should give more incentives to the exporters and decrease the cost of doing business to be more competitive than the regional economies such as India, Bangladesh, and China.

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“…The GDP is used as economic growth indicator (Sokolov-Mladenović, Milovančević, Mladenović & Alizamir, 2016). Estimation of electric energy demand in Turkey using ANN with teaching-learning-based optimization is studied.…”

mentioning

confidence: 99%