Machine Learning Algorithms: Accessing World Bank Database & Google Trends to Predict Economic Cycle

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 Introductio n 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.

Introductio n

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.

Conclusio n

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 non- stationary 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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