Time Series Analysis

Marc Goetschalckx ()
Additional contact information
Marc Goetschalckx: Georgia Institute of Technology

Chapter Chapter 4 in Supply Chain Engineering, 2011, pp 75-126 from Springer

Abstract: Abstract Recall that in the previous chapter objective or quantitative forecasting methods were defined as forecasting methods that rely on a formalized underlying model to make predictions. They are further divided into time series and causal methods. Time series analysis is a forecasting method based on the fundamental assumption that future estimates are based on prior, historical values of the same variable. This implies that the historical pattern exhibited by the variable to be forecasted will extend into the future. In addition, it is implicitly assumed that historical data are available. The only independent variable in a forecasting model based on time series analysis is the time period. Time series forecasting methods are mostly used to forecast variables for the short to intermediate term. As such, time series methods are some of the forecasting techniques most often used by logisticians and are developed in further detail in this chapter.

Keywords: Forecast Error; Data Pattern; Forecast Method; Seasonal Forecast; Exponential Smoothing (search for similar items in EconPapers)
Date: 2011
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:isochp:978-1-4419-6512-7_4

Ordering information: This item can be ordered from
http://www.springer.com/9781441965127

DOI: 10.1007/978-1-4419-6512-7_4

Access Statistics for this chapter

More chapters in International Series in Operations Research & Management Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().