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Quantitative Endosurgery Process Analysis by Machine Learning Method Bojan Nokovic , Andrew Lambe (pages: 1-7) Modelling Student Performance in a Structural Steel Graduate-Based Module: A Comparative Analysis Between K-Nearest Neighbor and Dummy Classifiers Masengo Ilunga , Omphemetse Zimbili , Phahlani Mampilo , Agarwal Abhishek (pages: 8-15) Interoperable Digital Skills for Foreign Languages Education in the COVID-19 Paradigm Rusudan Makhachashvili , Ivan Semenist , Iryna Vorotnykova (pages: 16-20) Education, Training and Informatics Go Hand in Hand in (Foreign) Higher Education Institutions (HEIs) – Case Studies From Live and Online Classrooms Ekaterini Nikolarea (pages: 21-29) Enhancing Pedagogical and Digital Competencies Through Digital Tools: A Proposal for Semi-schooled Language Teaching Programs in Oaxaca, Mexico José de Jesús Bautista Hernández , Eduardo Bustos Farías , Norma Patricia Maldonado Reynoso (pages: 30-35) Railway Track Degradation Modelling Using Finite Element Analysis: A Case Study in South Africa Ntombela Lunga , Masengo Ilunga (pages: 36-50) Continuum of Academic Collaboration: Issues of Inconsistent Terminology in Multilingual Context Cristo Leon , James Lipuma , Marcos O. Cabobianco , Maria B. Daizo (pages: 51-62) Peat Resource Management and Climate Change Mitigation Issues – Case of Latvia Anita Titova , Natalja Lace (pages: 63-70) Using Geospatial Computation Intelligence for Mapping Temporal Evolution of Urban Built-up in Selected Areas of the Ekurhuleni Municipality, South Africa Jo-Anne Correia , Masengo Ilunga (pages: 71-80) Cybernetics and Informatics of Generative AI for Transdisciplinary Communication in Education Rusudan Makhachashvili , Ivan Semenist (pages: 81-88) Navigating Psychological Riptides: How Seafarers Cope and Seek Help for Mental Health Needs Coleen Abadicio , Stella Louise Arenas , Rosette Renee Hahn , Angel Berry Maleriado , Ramon Miguel Mariano , Rodolfo Antonio Ma. Zabella , Genejane Adarlo (pages: 89-98)
ABSTRACT
Analysis of Leading Economic Indicator Data and Gross Domestic Product Data Using Neural Network Methods
In this report, Leading Economic Indicator (LEI) data
and Gross Domestic Product (GDP) data have been
analyzed to determine if changes in the ten indicators
can be used to predict changes in GDP. Three neural
network methods and one statistical method were used
to complete the analysis. For this project, the intent
was to use multiple regression and backpropagation to
develop correlations in which LEI values are used to
predict the GDP change in the following quarter.
Alternatively, Kohonen's self-organizing map and
hierarchical clustering were used to group months of
LEI data into clusters to determine if months in a
cluster (and thus months with similar LEI values) also
have similar changes in GDP.
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