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  <titleInfo>
    <title>Business forecasting</title>
    <subTitle>the emerging role of artificial intelligence and machine learning</subTitle>
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  <name type="personal">
    <namePart>Gilliland, Michael</namePart>
    <role>
      <roleTerm type="text">editor.</roleTerm>
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  </name>
  <name type="personal">
    <namePart>Tashman, Len</namePart>
    <namePart type="date">1942-</namePart>
    <role>
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  </name>
  <name type="personal">
    <namePart>Sglavo, Udo</namePart>
    <namePart type="date">1968-</namePart>
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    <dateIssued encoding="marc">2021</dateIssued>
    <issuance>monographic</issuance>
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  <language>
    <languageTerm authority="iso639-2b" type="code">eng</languageTerm>
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  <physicalDescription>
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    <extent>xvii, 414 pages ; 26 cm.</extent>
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  <abstract>"Forecasting is the process of making predictions of the future based on past and present data and most commonly by analysis of trends. A commonplace example might be estimation of some variable of interest at some specified future date. Prediction is a similar, but more general term This book provides ideas from the most important and influential authors in the field of forecasting on an array of topics that are highly relevant. It provides multiple perspectives on relevant issues like monitoring forecast performance, forecasting process, communication and accountability for the forecast, the use of big data in forecasting, and the role of AI/ML in enhancing traditional time series forecasting methods. Note: Content is mostly material previously published in "practitioner" journals (Foresight and Journal of Business Forecasting), with a few articles from the academic International Journal of Forecasting. Some articles report on academic research, or include case studies, but most are thoughtful discussion of important business forecasting topics, such as the role of the sales force in forecasting, or the value of judgmental overrides to a statistical forecast, or how to determine what forecast error is "avoidable." Articles were chosen for their importance, influence, informativeness, and for being provocative -- leading the reader to new considerations and ideas"--</abstract>
  <tableOfContents>Artificial intelligence and machine learning in forecasting -- Big data in forecasting -- Forecasting methods: modeling, selection, and monitoring -- Forecasting performance -- Forecasting process: communication, accountability, and S&amp;OP -- Afterwords: essays on topics in business forecasting.</tableOfContents>
  <note type="statement of responsibility">edited by Michael Gilliland, Len Tashman, and Udo Sglavo.</note>
  <note>Includes bibliographical references and index.</note>
  <subject authority="lcsh">
    <topic>Business forecasting</topic>
  </subject>
  <subject authority="lcsh">
    <topic>Artificial intelligence</topic>
  </subject>
  <subject authority="lcsh">
    <topic>Machine learning</topic>
  </subject>
  <classification authority="lcc">HD30.27 .B874 2021</classification>
  <classification authority="ddc" edition="23">658.40355028563 B964</classification>
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    <titleInfo>
      <title>Business forecasting</title>
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      <publisher>Hoboken, New Jersey : Wiley, 2021</publisher>
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    <identifier type="local">(DLC) 2021002142</identifier>
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  <identifier type="isbn">9781119782476</identifier>
  <identifier type="lccn">2021002141</identifier>
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