Time Series Forecasting

17 Pages Posted: 1 Jun 2017

See all articles by Samuel E. Bodily

Samuel E. Bodily

University of Virginia - Darden School of Business


This technical note introduces (1) approaches to forecasting in general, (2) simple moving averages and exponential smoothing, (3) accounting for seasonality in forecasting, (4) accounting for trend in forecasting, and (5) implementing a forecasting model. Holt and Winter models for exponential smoothing are included.




I. Introduction

In many business situations, we attempt to forecast what will happen based on the information at hand. Often the task is to predict the next of a series of periodic observations of a quantity (for example: demand for a product). The observations form what is called a time series. When we rely solely on past observations of that quantity to predict future occurrences, the approach is called time series forecasting. Although time series forecasting does not assume that the future business results will be the same as in the past, it does rely on the premise that past patterns in the time series will carry on into the future.

A. Forecast uses

Forecasts are useful for planning activities in the operations, marketing, financial, and perhaps all other functions of business. The time horizon of a forecast indicates how far into the future it predicts. The required time horizon is one of the determinants of which approach to use in forecasting. In this note, we address short-term forecasts, that is, from one to a few periods into the future.

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Keywords: data analysis, forecasting, regression analysis, time series

Suggested Citation

Bodily, Samuel E., Time Series Forecasting. Darden Case No. UVA-QA-0438. Available at SSRN: https://ssrn.com/abstract=2975069

Samuel E. Bodily (Contact Author)

University of Virginia - Darden School of Business ( email )

P.O. Box 6550
Charlottesville, VA 22906-6550
United States
434-924-4813 (Phone)
434-293-7677 (Fax)

HOME PAGE: http://www.darden.virginia.edu/faculty/bodily.htm

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