DOCSLIB.ORG

Finding the Face of Your Data 

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Finding the Face of Your Data  Finding the Face of Your Data There’s been an explosion in data assets Growth of the “digital universe”1 Data overload in context2 40,000 1 EB = 1 billion gigabytes x70 30,000 20,000 10,000 Exabytes 2009 2020 IDC estimates that “tagged” information accounts for only about 3% The amount of information generated by humanity during the first of the digital universe, with analyzed information at 0.5%. The value day of a baby’s life today is equivalent to 70 times the information of big data technology lies in exploring the “untapped pools.” contained in the Library of Congress. Enterprise expectations are as big as the data Big data spending forecast by component3 50 Compute 31% CAGR Storage 40 Networking 30 Infrastructure Software SQL Database Software 20 NoSQL Database Software Application Software 10 Professional Services Billion Dollars Xaas 2011 2012 2013 2014 2015 2016 2017 According to a Wikibon study, big data spend will shift from infrastructure and middleware to value-add services and software during the next five years. Infrastructure, middleware, and technical services will likely become increasingly commoditized as they mature and common standards are adopted. We also note that this study did not include the costs associated with the business and domain experts’ time – a critical element of actionable insight. Taking advantage of data requires new tools ... Traditional and non-traditional value in data Data diving Pattern finding In taking advantage of new data assets – The other side of that same coin, from internal, external, structured, and seeking and patterning for previously unstructured data – and analytics tools, unasked and unanswerable questions, the most common form of value is realized is less common, but potentially more through exploiting deeper detail for new important to the enterprise. and better answers to current questions. Expanding the data analytics toolbox NoSQL market4 4 3 21% CAGR 2 1 Visualization Natural language Billion processing dollars 2013 2014 2015 2016 2017 2018 The worldwide NoSQL market is expected to reach $3.4 billion by 2018. The market has shifted from community- to application-driven as venture capital funding, mergers, and product offerings increase. 5 Ontology discovery Machine learning Hadoop & MapReduce ecosystem software market 1000 750 60.2% CAGR 500 250 Quantitative modeling Text analytics Million dollars 2011 2012 2013 2014 2015 2016 In order to extract both types of value from data, new techniques and An IDC forecast taking into account software, maintenance, and tools are likely required. They may sound esoteric and academic, but software-as-a-service revenue predicts the Hadoop and MapReduce they are enterprise-caliber and now fundamental. ecosystem software market will reach almost $813 million by 2016. However, other experts say this estimate is conservative, underestimating growth in cloud-based offerings and not fully considering all the positive externalities – that “every sale made, fraud thwarted or page view generated thanks to Hadoop means a healthier economy.” ... As well as new team members with specialized skillsets Google Trends: Searches for “data scientist”6 LinkedIn: Analytics & data science job growth7 100 0.1 Peak 80 0.08 60 0.06 40 0.04 20 0.02 Search Percentage of interest job starters January 2011 July 2013 1990 2010 Using these new tools and techniques may require skills such as data science, creative design, and cultural anthropology, which you may not already have in the enterprise. New team members with these capabilities should represent a blend of technology and business domain expertise. The new job title with the most fanfare has been the data scientist. A profile of 12 leading data scientists Letters represent individuals, colors represent current titles, circle size represents number of individuals. Consulting Entertain- Health Telecomm. Internet ment M INDUSTRY F G L C K A B D J E H Director Business of data & analyst Data scientist analytics CURRENT TITLE A C D E F G H B M J K L PREVIOUS JOB Researcher, Director Product Director of Owner of data & Business Data Software manager Research analytics analyst scientist engineer CE J B A C F D H G K L M EDUCATION Statistics Economics Information Engineering & Math & Finance systems Chemistry Business Computer & Physics science Harvard Business Review recently called the data scientist the “Sexiest Job of the 21st Century.”8 But, finding data scientists and data professionals with both IT and line-of-business knowledge can be difficult. The diagram above shows the career paths, industries, and educational backgrounds of 12 leading data professionals aggregated with publicly available social network information. BOTTOM LINE The right balance of people, data, and computing power can reveal questions that previously couldn’t be answered – or even asked – to enhance data-driven business decisions and actions on insights For more information please visit www.deloitte.com/us/techtrends2013. SOURCES 1 IDC, “The Digital Universe in 2020: Big Data, Bigger Digital Shadows, and Biggest Growth in the Far East,” December 2012. 2 Rick Smolan and Jennifer Erwitt, The Human Face of Big Data (Against All Odds Productions, 2012). 3 Kelly, J. Big Data Market Forecast by Component. Retrieved July 31, 2013, from http://wikibon.org/wiki/v/ Big_Data_Vendor_Revenue_and_Market_Forecast_2012-2017. 4 NoSQL Market Forecast 2013-2018, http://www.marketresearchmedia.com/?p=568 (September 11, 2012). 5 Derrick Harris, All aboard the Hadoop money train, http://gigaom.com/2012/05/07/all-aboard-the-hadoop-money-train (May 7, 2012). 6 Google Trends, http://www.google.com/trends, accessed August 7, 2013. 7 Gil Press, A Very Short History Of Data Science, http://www.forbes.com/sites/gilpress/2013/05/28/a-very-short-history-of-data-science (May 28, 2013). 8 Thomas H. Davenport and D.J. Patil, Data Scientist: The Sexiest Job of the 21st Century, http://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century (October 2012). This publication contains general information only and Deloitte is not, by means of this publication, rendering accounting, business, financial, investment, legal, tax, or other professional advice or services. This publication is not a substitute for such professional advice or services, nor should it be used as a basis for any decision or action that may affect your business. Before making any decision or taking any action that may affect your business, you should consult a qualified professional advisor. Deloitte shall not be responsible for any loss sustained by any person who relies on this publication. As used in this document, "Deloitte" means Deloitte Consulting LLP, a subsidiary of Deloitte LLP. Please see www.deloitte.com/us/about for a detailed description of the legal structure of Deloitte LLP and its subsidiaries. Certain services may not be available to attest clients under the rules and regulations of public accounting. Copyright © 2013 Deloitte Development LLC. All rights reserved..
Load more

Recommended publications
  • UC Riverside UCR Honors Capstones 2016-2017
    UC Riverside UCR Honors Capstones 2016-2017 Title GTWENDS Permalink https://escholarship.org/uc/item/41g0w0q9 Author Asfour, Mark Publication Date 2017-12-08 Data Availability The data associated with this publication are within the manuscript. eScholarship.org Powered by the California Digital Library University of California GTWENDS By Mark Jeffrey Asfour A capstone project submitted for Graduation with University Honors October 20, 2016 University Honors University of California, Riverside APPROVED ______________________________ Dr. Evangelos Christidis Department of Computer Science & Engineering ______________________________ Dr. Richard Cardullo, Howard H Hays Chair and Faculty Director, University Honors Associate Vice Provost, Undergraduate Education Abstract GTWENDS is an online interactive map of the United States of America that displays the locations of trending Twitter tweets, Google Search trends, and Google Hot Trends topics. States on the map are overlaid with a blue color where Twitter trends originate and a red color where Google trends originate with respective opacity levels varying based on the levels of interest from each website. Through the use of web crawling, map-reducing, and utilizing distributed processing, this project allows visitors to have an interactive geographic visual representation of current national social media topic activities. Visitors can use GTWENDS to learn about social media trends by observing where trends originate from, comparing the contrasts and similarities between trends from Twitter and Google, understanding what types of events trigger mass social media sharing, and much more. ii Acknowledgements I have worked on and developed GTWENDS with my classmates, Mehran Ghamaty (University of California, Riverside Spring 2016) and Jacob Xu (University of California, Riverside Spring 2016), during our senior design project class, CS 179G Databases Spring 2016, under the supervision of my professor and mentor, Professor Evangelos Christidis.
    [Show full text]
  • Unit 4 Lesson 1
    Unit 4 Lesson 1 What is Big Data? Resources Name(s)_______________________________________________ Period ______ Date ________________ Unit 4 Lesson 01 Activity Guide - Big Data Sleuth Card Directions: Web Sites: ● With a partner, select one of the tools in the list to the right. 1. Web archive http://www.archive.org ​ ● Determine what the tool is showing. 2. Measure of America http://www.measureofamerica.org/maps/ ​ ● Find the source of the data it allows you to explore. 3. Wind Sensor network http://earth.nullschool.net/ ​ ● Complete the table below. 4. Twitter sentiment https://www.csc.ncsu.edu/faculty/healey/tweet_viz/tweet_app/ ​ 5. Alternative Fuel Locator http://www.afdc.energy.gov/locator/stations/ ​ Website Name What is this website potentially useful for? What ​ kinds of problems could the provided information be used to solve? Is the provided visualization useful? Does it provide insight into the data? How does it help you look at a lot of information at once? How could it improve? Where is the data coming from? Check for “About”, “Download”, or “API”. You may also need to do a web search. ● Is the data from one source or many? ● Is it static or live? ● Is the source reputable? Why or why not? ● Add a link to the raw data if you can find one. Do you consider this “big” data? Explain your reasoning. 1 Unit 4 Lesson 2 Finding Trends with Visualizations Resources Unit 4 Lesson 2 Name(s)_______________________________________________ Period ______ Date ___________________ Activity Guide - Exploring Trends What’s a Trend? When you post information to a social network, watch a video online, or simply search for information on a search engine, some of that data is collected, and you reveal what topics are currently on your mind.
    [Show full text]
  • Apache Hadoop & Spark – What Is It ?
    Hadoop { Spark Overview J. Allemandou Generalities Hadoop Spark Apache Hadoop & Spark { What is it ? Demo Joseph Allemandou JoalTech 17 / 05 / 2018 1 / 23 Hello! Hadoop { Spark Overview Joseph Allemandou J. Allemandou Generalities Hadoop Spark Demo 2 / 23 Plan Hadoop { Spark Overview J. Allemandou Generalities Hadoop Spark 1 Generalities on High Performance Computing (HPC) Demo 2 Apache Hadoop and Spark { A Glimpse 3 Demonstration 3 / 23 More computation power: Scale up vs. scale out Hadoop { Spark Overview J. Allemandou Scale Up Scale out Generalities Hadoop Spark Demo 4 / 23 More computation power: Scale up vs. scale out Hadoop { Spark Overview Scale out J. Allemandou Scale Up Generalities Hadoop Spark Demo 4 / 23 Parallel computing Hadoop { Spark Overview Things to consider when doing parallel computing: J. Allemandou Partitioning (tasks, data) Generalities Hadoop Spark Communications Demo Synchronization Data dependencies Load balancing Granularity IO Livermore Computing Center - Tutorial 5 / 23 Looking back - Since 1950 Hadoop { Spark Overview J. Allemandou Generalities Hadoop Spark Demo Figure: Google Ngram Viewer 6 / 23 Looking back - Since 1950 Hadoop { Spark Overview J. Allemandou Generalities Hadoop Spark Demo Figure: Google Ngram Viewer 6 / 23 Looking back - Since 1950 Hadoop { Spark Overview J. Allemandou Generalities Hadoop Spark Demo Figure: Google Ngram Viewer 6 / 23 Looking back - Recent times Hadoop { Spark Overview J. Allemandou Generalities Hadoop Spark Demo Figure: Google Trends 7 / 23 Same problem, different tools Hadoop { Spark Overview J. Allemandou Generalities Supercomputer Big Data Hadoop Spark Demo Dedicated hardware Commodity hardware Message Passing Interface 8 / 23 MPI Hadoop { Spark Overview J. Allemandou Generalities C / C++ / Fortran / Python Hadoop Spark Demo Low-level API - Send / receive messages a lot to do manually split the data assign tasks to workers handle synchronisation handle errors 9 / 23 Hadoop + Spark Hadoop { Spark Overview J.
    [Show full text]
  • Latency: Certain Operations Have a Much Higher Latency Than Other Operations Due to Network Communication
    Gi2M+v "B; .i MHvbBb rBi? a+H M/ aT`F >2i?2` JBHH2` Distribution Distribution introduces important concerns beyond what we had to worry about when dealing with parallelism in the shared memory case: 111-- Partial failure: crash failures of a subset of the machines involved in a distributed computation . ...,. Latency: certain operations have a much higher latency than other operations due to network communication. Distribution Distribution introduces important concerns beyond what we had to worry about when dealing with parallelism in the shared memory case: 111-- Partial failure: crash failures of a subset of the machines involved in a distributed computation . ...,. Latency: certain operations have a much higher latency than other operations due to network communication. Latency cannot be masked completely; it will be an important aspect that also impacts the programming model. Important Latency Numbers L 1 cache reference 0.5ns Branch mispredict 5ns L2 cache reference 7ns Mutex lock/unlock 25ns Main memory reference l00ns Compress lK bytes with Zippy 3,000ns == 3µs Send 2K bytes over lGbps network 20,000ns == 20µs SSD random read 150,000ns == 150µs Read 1 MB sequentially from 250,000ns == 250µs Roundtrip within same datacenter 500,000ns == 0.5ms Read 1MB sequentially from SSD 1,000,000ns == lms Disk seek 10,000,000ns == l0ms Read 1MB sequentially from disk 20,000,000ns == 20ms Send packet US ---+ Europe ---+ US 150,000,000ns == 150ms Original compilation by Jeff Dean & Peter Norvig, w/ contributions by Joe Hellerstein & Erik Meijer
    [Show full text]
  • Unemployment Rate Forecasting Using Google Trends Bachelor Thesis in Econometrics & Operations Research
    Unemployment rate forecasting using Google trends Bachelor Thesis in Econometrics & Operations Research erasmus university rotterdam erasmus school of economics Author: Olivier O. Smit, 415283 Supervisor: Jochem Oorschot July 8, 2018 Abstract In order to make sound economic decisions, it is of great importance to be able to predict and interpret macro-economic variables. Researchers are therefore seeking continuously to improve the prediction performance. One of the main economic indicators is the US unemployment rate. In this paper, we empirically analyze whether, and to what extent, Google search data have additional predictive power in forecasting the US unemployment rate. This research consists of two parts. First, we look for and select Google search data with potential predicitive power. Second, we evaluate the performance of level and directional forecasts. Here, we make use of different models, based on both econometric and machine learning techniques. We find that Google trends improve the predictive accuracy in all used forecasting methods. Lastly, we discuss the limitations of our research and possible future research suggestions. 1 1 Introduction Nowadays, search engines are intensely used platforms. They serve as the gates to the Internet and at the same time help users to create order in the immense amount of websites and data available on the Internet. About a decade ago, researchers have realized that these search engines contain an enormous quantity of new data that can be of great additional value for modeling all kinds of processes. Previous researches have already proven this to be true. For example, Constant and Zimmermann (2008) have shown that including Google - the largest search engine in terms of search activity and amount of users - query data can be very useful in measuring economic processes and political activities.
    [Show full text]
  • 2016 Google Food Trends Report
    Food Trends 2016 U.S. Report [email protected] Intro With every query typed into a search bar, we are given a glimpse into user considerations or intentions. By compiling top searches, we are able to render a strong representation of the United States’ population and gain insight into this population’s behavior. In our Google Food Trends Report, we are excited to bring forth the power of data into the hands of the marketers, product developers, restaurateurs, chefs, and foodies. The goal of this report is to share useful data for planning purposes accompanied by curated styles of what we believe can make for impactful trends. We are proud to share this iteration and look forward to hearing back from you. Jon Lorenzini | Senior Analytics Lead, Food & Beverage Lisa Lovallo | Global Insights Manager, Food & Beverage Olivier Zimmer | Trends Data Scientist Yarden Horwitz | Trends Brand Strategist Methodology To compile a list of accurate trends within the food industry, we pulled top volume queries related to the food category and looked at their monthly volume from January 2014 to February 2016. We first removed any seasonal effect, and then measured the year-over-year growth, velocity, and acceleration for each search query. Based on these metrics, we were able to classify the queries into similar trend patterns. We then curated the most significant trends to illustrate interesting shifts in behavior. Query De-seasonalized Trend 2004 2006 2008 2010 2012 2014 Query 2016 Characteristics Part One Part Two Part Three top risers a spotlight on an extensive list of and decliners top trending the top volume themes food searches Trend Categories To identify top trends, we categorized past data into six different clusters based on similar behaviors.
    [Show full text]
  • Google Benefit from News Content
    Google Benefit from News Content Economic Study by News Media Alliance June 2019 EXECUTIVE SUMMARY: The following study analyzes how Google uses and benefits from news. The main components of the study are: a qualitative overview of Google’s usage of news content, an analysis of news content on Google Search, and an estimate of revenue Google receives from news. I. GOOGLE QUALITATIVE USAGE OF NEWS ▪ News consumption increasingly shifts towards digital (e.g., 93% in U.S. get some news online) ▪ Google has increasingly relied on news to drive consumer engagement with its products ▪ Some examples of Google investment to drive traffic from news include: o Significant algorithmic updates emphasize news in Search results (e.g., 2011 “Freshness” update emphasized more recent search results including news) ▪ Google News keeps consumers in the Google ecosystem; Google makes continual updates to Google News including Subscribe with Google (introduced March 2018) ▪ YouTube increasingly relies on news: in 2017, YouTube added “Breaking News;” in 2018, approximately 20% of online news consumers in the US used YouTube for news ▪ AMPs (accelerated mobile pages) keep consumers in the Google ecosystem II. GOOGLE SEARCH QUANTITATIVE USAGE OF NEWS CONTENT A. Key statistics: ▪ ~39% of results and ~40% of clicks on trending queries are news results ▪ ~16% of results and ~16% of clicks on the “most-searched” queries are news results B. Approach ▪ Scraped the page one of desktop results from Google Search o Daily scrapes from February 8, 2019 to March 4, 2019
    [Show full text]
  • Predicting the Present with Google Trends
    Predicting the Present with Google Trends Hyunyoung Choi, Hal Varian∗ December 18, 2011 Abstract In this paper we show how to use search engine data to forecast near-term values of economic indicators. Examples include automobile sales, unemployment claims, travel destination planning, and consumer confidence. Government agencies periodically release indicators of the level of economic activity in various sectors. However, these releases are typically only available with a reporting lag of several weeks and are often revised a few months later. It would clearly be helpful to have more timely forecasts of these economic indicators. Nowadays there are several sources of data on real-time economic activity available from private sector companies such as Google, MasterCard, Federal Express, UPS, Intuit and many others. In this paper we examine Google Trends, which is a a real-time daily and weekly index of the volume of queries that users enter into Google. We have found that these query indices are often correlated with various economic indicators and may be helpful for short-term economic prediction. We are not claiming that Google Trends data can help in predicting the future. Rather we are claiming that Google Trends may help in predicting the present. For example, the volume of queries on automobile sales during the second week in June may be helpful in predicting the June auto sales report which is released several weeks later in July. It may also be true that June queries help to predict July sales, but we leave that question for future research, as this depends very much on the particular time series in question.
    [Show full text]
  • Searching for Information Online: Using Big Data to Identify The
    C O R P O R A T I O N Searching for Information Online Using Big Data to Identify the Concerns of Potential Army Recruits Salar Jahedi, Jennie W. Wenger, Douglas Yeung SUMMARY ■ In this report, we assess some Key findings empirical applications of web search data and discuss the prospective value such data can offer to Army recruiting • Google search queries can be used to better under- stand how interest in military careers has evolved over efforts. We discuss three different tools—Google Trends, time and geographic location. Google AdWords, and Google Correlate—that can be used to access and analyze readily available, anonymous • It is possible to use these tools to identify the chief data from Internet searches related to the Army and to Army-related concerns that potential recruits have, Army service. We find that Google search queries can be including the qualifications for, procedures for, or ben- efits of enlisting. used to better understand how interest in military careers has evolved over time and geographic location, and even • It is possible to predict with reasonable accuracy what identify the foremost Army-related concerns that potential individuals were searching for months before or after recruits experience. Moreover, it is possible to predict with searching for Army-related terms reasonable accuracy what non-Army related terms people • Including Google Trends terms in a model of factors are searching for in the months before or after an Army influencing the number of Army accessions increases query. Finally, our results suggest that search terms can the predictive power of the model.
    [Show full text]
  • Using Google Trends in Management Fashion Research: a Short Note Dag Øivind Madsen
    Using Google Trends in Management Fashion Research: A Short Note Dag Øivind Madsen To cite this version: Dag Øivind Madsen. Using Google Trends in Management Fashion Research: A Short Note. 2016. hal-01343880 HAL Id: hal-01343880 https://hal.archives-ouvertes.fr/hal-01343880 Preprint submitted on 10 Jul 2016 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. USING GOOGLE TRENDS IN MANAGEMENT FASHION RESEARCH: A SHORT NOTE Dag Øivind Madsen, University College of Southeast Norway Abstract Google Trends (GT) is an analytic tool for measuring and monitoring Internet search data. In recent years GT been utilized for research in fields as diverse as health care, political science and economics. This short paper looks at the possibilities of using GT in management fashion research. GT could have a natural application in the study of management fashion. After all, a key question of interest to management fashion researchers is how the popularity of management concepts and ideas evolves over time. The paper discusses the pros and cons of using GT in management fashion research. Using Internet search data can possibly reveal intentions and expectations in the management fashion market, provide indicators of future fashion demand, and as well as indicate “outbreaks” of contagious management concepts and ideas.
    [Show full text]
  • FTC-Google Antitrust Primer
    FTC‐Google Antitrust Primer: Top Ten Q&A 1. Why is the FTC Investigating Google? 2. Why Isn’t Competition “One Click Away?” (graphic) 3. Why Does Google Have a Search Advertising Monopoly? (graphic) 4. How are Consumers and Innovation Harmed by Google? 5. What Makes Google’s Monopoly Lasting? (graphic) 6. How Does Google Search Discriminate Against Competitors? (graphic) 7. How Have Google’s 98 Acquisitions Foreclosed Competition? (graphic) 8. What is Google’s Monopolization Strategy? 9. How Can One Explain the Google Antitrust Problem to a Non‐Expert? 10. Why is Google’s Denial of Conflicts a Deceptive Trade Practice? By Scott Cleland** President, Precursor LLC*** [email protected] www.Googleopoly.net & www.GoogleMonitor.com June 27, 2011 * See Appendix D for links to Googleopoly I‐VII research series. ** The views expressed in this presentation are the author’s; see Scott Cleland’s full biography at: www.ScottCleland.com ***Precursor LLC serves Fortune 500 clients, some of which are Google competitors. 1. Why is the FTC Investigating Google? 1. FTC Promised to Monitor Google Closely: In approving Google’s acquisition of DoubleClick (12/07) in a 4‐1 vote, the FTC said: “We want to be clear, however, that we will closely watch these markets and, should Google engage in unlawful tying or other anticompetitive conduct, the Commission intends to act quickly.” 2. Google is #1 Antitrust Violator: U.S. antitrust authorities have sanctioned/opposed Google five times, involving five different markets, in the last 30 months alone: DOJ threatened a monopolization suit in blocking the Google‐Yahoo Ad Agreement; DOJ twice opposed the Google Book Settlement in Federal Court; DOJ sanctioned Google with five other companies for collusion in hiring practices; FTC forced Google CEO off of Apple’s board; DOJ required court supervision of Google and its integration of ITA.
    [Show full text]
  • The Proper Use of Google Trends in Forecasting Models Arxiv
    The Proper Use of Google Trends in Forecasting Models Marcelo C. Medeiros∗ Pontificial Catholic University of Rio de Janeiro Henrique F. Pires Pontificial Catholic University of Rio de Janeiro First version: Fev 2021 This version: Mar 2021 Abstract It is widely known that Google Trends has become one of the most popular free tool used by forecasters both in academics and in the private and public sectors. There are many papers, from several different fields, concluding that Google Trends improve forecasts' accuracy. However, what seems to be widely unknown, is that each sample of Google search data is different from the other, even if you set the same search term, dataand location. This means that it is possible to find arbitrary conclusions merely by chance. This paper aims to show why and when it can become a problem and how to overcome this obstacle. Keywords: Google trends, forecasting, nowcasting, big data. arXiv:2104.03065v3 [econ.EM] 10 Apr 2021 ∗Updated versions of this paper will be available on the corresponding author's webpage. 1 Introduction Google Trends is an incredible Google tool, available at no cost, that shows the most popular terms searched in the recent past. It provides access to a sample of actual search requests made to Google, allowing one to look into the interest in a particular topic from around the globe or down to city-level geography. Several studies from different used this resource mainly to improve forecast accuracy. See, for example, Askitas and Zimmermann [2009], Artola and Galan [2012], Baker and Fradkin [2017], Naccaratoa et al.
    [Show full text]