Use of Machine Learning Algorithms: Accessing World Bank Database & Google Trends to Predict Economic Cycle - Statswork

1. USE OF MACHINE LEARNING ALGORITHMS: ACCESSING WORLD BANK DATABASE & GOOGLE TRENDS TO PREDICT ECONOMICCYCLE An Academic presentationby Dr. Nancy Agens, Head, Technical Operations, Statswork Group: www.statswork.com Email:info@statswork.com

2. Today'sDiscussion Outline ofTopics In Brief Introduction Use of World Bank Database & Google Trends Search Volume Index in Forecasting/Nowcasting Economic Variables Research Areas of Interest Research ProposalGuidelines You, PhDAssistance Research Lab and the University ofBirmingham

3. InBrief With the combination of math, statistics, and computer science, the big data analysisand ML algorithms are becoming more and more computationallyemphasized. Google Trends data can aid advance in forecasts of the current level of activity for several different economic timeseries. 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 BankDatabase.

4. 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 ofdata. 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 commercialrecords. This phenomenon can reinforce analysis for decision-making, by providing more comprehensive, instantaneous and granular information as a counterpart to “traditional” economicindicators. Google Trendis presently one of the most common analytics tools noted by numerous studies and applying by policymakerunits. MLmethods have recently been anticipated as substitutes to time-series regression models typically used by World banks forpredicting main economicvariables.

5. Use of World Bank Database & Google Trends Search Volume Index in Forecasting/Nowcasting EconomicVariables

6. COUNTRY UNDERANALYSIS Germany UnitedKingdom Chile France, Italy, Portugal, Spain Germany France,Italy Portugal Turkey Spain UnitedKingdom United States China UnitedStates Japan VARIABLE TOPREDICT GDP Retailsales Car sales Carsales Unemployment rate Unemployment rate Unemployment rate Unemployment rate Unemployment rate Unemployment rate Unemployment rate Consumer priceindex Oilprices Stockprices/returns

7. The ultimate goal of this blog is to computationally forecast World Bank economic structure and Google trends by relating big dataandML. The Objective and Scope ofResearch Excitingly, from 2004 to 2017, mixed observations such as qualitative surveydetailsand 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 belowtable.

8. Table 1. The specifics of collective informationused to data science analyses and Big data from Google Trendsdatabase

9. Variable Definitions and DataSources

10. Contd..

12. ML algorithms for forecasting the big datain economic variables are totally different from traditionally parametric valuations and is morepowerful. Conclusion The ML systems can detect a vast amount of enlightening details in databases, including qualitative data, quantitative data, and time-seriestrends. ML systems can proficiently compute both stationary and non- stationarydata. Machine learning techniques can explain the outliners in the mixed remark rather than traditional econometric methods, which certainly needexpectations.

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