11. Time Series Models

  1. Paolo Brandimarte

Published Online: 24 MAY 2011

DOI: 10.1002/9781118023525.ch11

Quantitative Methods: An Introduction for Business Management

Quantitative Methods: An Introduction for Business Management

How to Cite

Brandimarte, P. (2011) Time Series Models, in Quantitative Methods: An Introduction for Business Management, John Wiley & Sons, Inc., Hoboken, NJ, USA. doi: 10.1002/9781118023525.ch11

Publication History

  1. Published Online: 24 MAY 2011
  2. Published Print: 4 APR 2011

ISBN Information

Print ISBN: 9780470496343

Online ISBN: 9781118023525



  • autoregressive models;
  • exponential smoothing method;
  • moving-average model;
  • quantitative forecasting methods;
  • statistical framework;
  • time series decomposition


Within the class of quantitative forecasting methods, an alternative to regression models is the family of time series models. The distinguishing feature of time series models is that they aim at forecasting a variable of interest, based only on observations of the variable itself; no explanatory variable is considered. This chapter first illustrates the fundamental ideas of time series decomposition, which highlights the possible presence of factors like trend and seasonality. Then, it deals with a very simple approach, moving average, which has a limited domain of applicability but is quite useful in pointing out basic tradeoffs that a person has to make when fine-tuning a forecasting algorithm. The chapter also describes the widely used family of exponential smoothing methods. Finally, it deals with autoregressive and moving-average models within a proper statistical framework.

Controlled Vocabulary Terms

autoregressive models; exponential smoothing; moving average; Moving-average model; quantitative forecasting; time series decomposition