use of machine learning algorithms: accessing world bank database & google trends to predict...
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Google Trend is presently one of the most common analytics tools noted by numerous studies and applied by policymaker units. The enormous challenge to relate the advanced computational method, called ML algorithms for forecasting the big data in economic variables are totally different from traditionally parametric valuations and is more powerful. The ML systems can detect a vast amount of enlightening details in databases, including qualitative data, quantitative data, and time-series trends. Statswork offers statistical services as per the requirements of the customers. When you Order statistical Services at Statswork, we promise you the following – Always on Time, outstanding customer support, and High-quality Subject Matter Experts. Why Statswork? Plagiarism Free | Unlimited Support | Prompt Turnaround Times | Subject Matter Expertise | Experienced Bio-statisticians & Statisticians | Statistics Across Methodologies | Wide Range Of Tools & Technologies Supports | Tutoring Services | 24/7 Email Support | Recommended by Universities Contact Us: Website: http://www.statswork.com/ Email: [email protected] UnitedKingdom: +44-1143520021 India: +91-4448137070 WhatsApp: +91-8754446690TRANSCRIPT
USE OF MACHINE LEARNING ALGORITHMS:ACCESSING WORLD BANK DATABASE & GOOGLETRENDS TO PREDICT ECONOMIC CYCLE
An Academic presentation by Dr. Nancy Agens, Head, Technical Operations, StatsworkGroup: www.statswork.comEmail: [email protected]
In BriefIntroductionUse of World Bank Database & Google Trends Search Volume Index in Forecasting/NowcastingEconomic VariablesResearch Areas of InterestResearch Proposal GuidelinesYou, PhDAssistance Research Lab and the University of Birmingham
Outline of Topics
Today's Discussion
In Brief
With the combination of math, statistics, and computer science, the big data analysis and MLalgorithms are becoming more and more computationally emphasized. Google Trends data can aid advance in forecasts of the current level of activity for severaldifferent economic time series. Collective variables using in this blog were perceived from the source agents who effectivelycollected data details from trends of the world for quickly accessing, for example, Google Trendsand World Bank Database.
Information and internet technology has accepted new web-based facilities that affect every aspect oftoday’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 electronicfootprints 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 andapplying by policymaker units.
ML methods have recently been anticipated as substitutes to time-series regression models typically usedby World banks for predicting main economic variables.
Introduction
Use of World Bank Database &Google Trends Search Volume
Index in Forecasting/NowcastingEconomic Variables
GermanyUnited Kingdom
ChileFrance, Italy, Portugal, Spain
GermanyFrance, Italy
PortugalTurkeySpain
United KingdomUnited States
ChinaUnited States
Japan
COUNTRY UNDER ANALYSIS
GDPRetail salesCar salesCar sales
Unemployment rateUnemployment rateUnemployment rateUnemployment rateUnemployment rateUnemployment rateUnemployment rate
Consumer price indexOil prices
Stock prices/returns
VARIABLE TO PREDICT
The ultimate goal of this blog is to computationally forecastWorld Bank economic structure and Google trends byrelating big data and ML.
Excitingly, from 2004 to 2017, mixed observations such asqualitative survey details and time-trend data series arebeing employed to do an econometric estimation by AIapproaches.
Some of the variables are labelled and presented in thebelow table.
The Objective andScope of Research
Table 1. The specifics ofcollective information usedto data science analysesand Big data from GoogleTrends database
Variable Definitions andData Sources
Contd..
ML algorithms for forecasting the big data in economic variablesare totally different from traditionally parametric valuations and ismore powerful.
The ML systems can detect a vast amount of enlightening detailsin databases, including qualitative data, quantitative data, andtime-series trends.
ML systems can proficiently compute both stationary and non-stationary data.
Machine learning techniques can explain the outliners in themixed remark rather than traditional econometric methods, whichcertainly need expectations.
Conclusion
