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Basic Concepts of Time Series Modeling

In: Data Driven Model Learning for Engineers

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  • Guillaume Mercère

    (Université de Poitiers)

Abstract

This book dedicated to data-driven model learning with applications to univariate time series starts explains why, at the era of “big data,” being able to: Understand the links between the available data sets Make sensible forecasts from the available data sets are both essential in many fields for reliable decision making. In order to reach this goal, a standard and efficient first ingredient to analyze raw data and find trends to answer different strategic questions is model learning. Indeed, once reliable models of data set dynamical evolution are determined accurately, efficient prediction or classification tools can be deployed effectively. The developments introduced in this textbook do not depart from this well-tried methodology by bringing to light (new) solutions for data-driven model learning.

Suggested Citation

  • Guillaume Mercère, 2023. "Basic Concepts of Time Series Modeling," Springer Books, in: Data Driven Model Learning for Engineers, chapter 0, pages 1-7, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-31636-4_1
    DOI: 10.1007/978-3-031-31636-4_1
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