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Armstrong, J. S. (2001). Principles of Forecasting: A Handbook for Researchers and Practitioners. New York: Springer Science and Business Media.
https://doi.org/10.1007/978-0-306-47630-3

has been cited by the following article:

• TITLE: Conditionalism as a Methodology of Forecasting, Decision-Making and Machine Learning

  AUTHORS: Andrey Krakovsky

  KEYWORDS: Decision-Making, Machine Learning, Forecasting, Financial Markets

  JOURNAL NAME: Voice of the Publisher, Vol.8 No.1, February 11, 2022

  ABSTRACT: We examine how conditionality inherent to the real-life training data sets influences forecasting and decision-making by analyzing examples that range from behavioral logic to machine learning. For machine learning or other systematic methods, we outline a process-driven approach to better navigate and improve decisions in situations with obscure or difficult to capture conditionality. We outline implementation of these techniques in a portfolio management engine.