Database Reference
In-Depth Information
8.1 Overview of Time Series Analysis
Time series analysis attempts to model the underlying structure of observations
taken over time. A
time series
, denoted , is an ordered sequence of
equally spaced values over time. For example,
Figure 8.1
provides a plot of the
monthly number of international airline passengers over a 12-year period.
Figure 8.1
Monthly international airline passengers
In this example, the time series consists of an ordered sequence of 144 values. The
analyses presented in this chapter are limited to equally spaced time series of one
variable. Following are the goals of time series analysis:
• Identify and model the structure of the time series.
• Forecast future values in the time series.
Time series analysis has many applications in finance, economics, biology,
engineering, retail, and manufacturing. Here are a few specific use cases:
•
Retail sales:
For various product lines, a clothing retailer is looking to
forecast future monthly sales. These forecasts need to account for the
seasonal aspects of the customer's purchasing decisions. For example, in
the northern hemisphere, sweater sales are typically brisk in the fall season,
and swimsuit sales are the highest during the late spring and early summer.
Thus, an appropriate time series model needs to account for fluctuating
demand over the calendar year.
•
Spare parts planning:
Companies' service organizations have to forecast
future spare part demands to ensure an adequate supply of parts to repair
customer products. Often the spares inventory consists of thousands of
distinct part numbers. To forecast future demand, complex models for each
part number can be built using input variables such as expected part failure
