Prediction Revisited. Mark P. Kritzman

Copyright © 2022 by Megan Czasonis, Mark Kritzman, and David Turkington. All rights reserved.

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       Library of Congress Cataloging-in-Publication Data is Available:

      ISBN 9781119895589 (hardback)

      ISBN 9781119895602 (ePDF)

      ISBN 9781119895596 (epub)

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Timeline of Innovations

      Relevance is the centerpiece of our approach to prediction. The key concepts that give rise to relevance were introduced over the past three centuries, as illustrated in this timeline. In Chapter 8, we offer more detail about the people who made these groundbreaking discoveries.

Essential Concepts

      This book introduces a new approach to prediction, which requires a new vocabulary—not new words, but new interpretations of words that are commonly understood to have other meanings. Therefore, to facilitate a quicker understanding of what awaits you, we define some essential concepts as they are used throughout this book. And rather than follow the convention of presenting them alphabetically, we present them in a sequence that matches the progression of ideas as they unfold in the following pages.

       Observation: One element among many that are described by a common set of attributes, distributed across time or space, and which collectively provide guidance about an outcome that has yet to be revealed. Classical statistics often refers to an observation as a multivariate data point.

       Attribute: A recorded value that is used individually or alongside other attributes to describe an observation. In classical statistics, attributes are called independent variables.

       Outcome: A measurement of interest that is usually observed alongside other attributes, and which one wishes to predict. In classical statistics, outcomes are called dependent variables.

       Arithmetic average: A weighted summation of the values of attributes or outcomes that efficiently aggregates the information contained in a sample of observations. Depending on the context and the weights that are used, the result may be interpreted as a typical value or as a prediction of an unknown outcome.

       Spread: The pairwise distance between observations of an attribute, measured in units of surprise. We compute this distance

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