USE OF MACHINE LEARNING ALGORITHMS:
ACCESSING WORLD BANK DATABASE & GOOGLE
TRENDS TO PREDICT ECONOMIC CYCLE
An Academic presentation by
Dr. Nancy Agens, Head, Technical Operations, Statswork
Group: www.statswork.com
Email: info@statswork.com
Today's Discussion
Outline of Topics
In Brief
Introduction
Use of World Bank Database & Google Trends Search Volume Index in Forecasting/Nowcasting
Economic Variables
Research Areas of Interest
Research Proposal Guidelines
You, PhDAssistance Research Lab and the University of Birmingham
In Brief
With the combination of math, statistics, and computer science, the big data analysis and ML
algorithms are becoming more and more computationally emphasized.
Google Trends data can aid advance in forecasts of the current level of activity for several
different economic time series.
Collective variables using in this blog were perceived from the source agents who effectively
collected data details from trends of the world for quickly accessing, for example, Google Trends
and World Bank Database.
Introduction
Information and internet technology has accepted new web-based facilities that affect every aspect of
today’s financial and commercial activity that generate massive amount of data.
World banks face a flow in “financial big data sets”, replicating the combination of new emerging electronic
footprints as well as large and rising financial, administrative and commercial records.
This phenomenon can reinforce analysis for decision-making, by providing more comprehensive,
instantaneous and granular information as a counterpart to “traditional” economic indicators.
Google Trend is presently one of the most common analytics tools noted by numerous studies and
applying by policymaker units.
ML methods have recently been anticipated as substitutes to time-series regression models typically used
by World banks for predicting main economic variables.
Use of World Bank Database &
Google Trends Search Volume
Index in Forecasting/Nowcasting
Economic Variables
COUNTRY UNDER ANALYSIS
Germany
United Kingdom
Chile
France, Italy, Portugal, Spain
Germany
France, Italy
Portugal
Turkey
Spain
United Kingdom
United States
China
United States
Japan
VARIABLE TO PREDICT
GDP
Retail sales
Car sales
Car sales
Unemployment rate
Unemployment rate
Unemployment rate
Unemployment rate
Unemployment rate
Unemployment rate
Unemployment rate
Consumer price index
Oil prices
Stock prices/returns
The Objective and
Scope of Research
The ultimate goal of this blog is to computationally forecast
World Bank economic structure and Google trends by
relating big data and ML.
Excitingly, from 2004 to 2017, mixed observations such as
qualitative survey details and time-trend data series are
being employed to do an econometric estimation by AI
approaches.
Some of the variables are labelled and presented in the
below table.
Table 1. The specifics of
collective information used
to data science analyses
and Big data from Google
Trends database
Variable Definitions and
Data Sources
Contd..
ML algorithms for forecasting the big data in economic variables
are totally different from traditionally parametric valuations and is
more powerful.
Conclusion
The ML systems can detect a vast amount of enlightening details
in databases, including qualitative data, quantitative data, and
time-series trends.
ML systems can proficiently compute both stationary and nonstationary data.
Machine learning techniques can explain the outliners in the
mixed remark rather than traditional econometric methods, which
certainly need expectations.
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