Database Reference
In-Depth Information
rates, service diagnostic effectiveness, forecasted new product shipments,
and forecasted trade-ins/decommissions. However, time series analysis
can provide accurate short-term forecasts based simply on prior spare part
demand history.
•
Stock trading:
Some high-frequency stock traders utilize a technique
called
pairs trading
. In pairs trading, an identified strong positive
correlation between the prices of two stocks is used to detect a market
opportunity. Suppose the stock prices of Company A and Company B
consistently move together. Time series analysis can be applied to the
difference of these companies' stock prices over time. A statistically larger
than expected price difference indicates that it is a good time to buy the
stock of Company A and sell the stock of Company B, or vice versa. Of
course, this trading approach depends on the ability to execute the trade
quickly and be able to detect when the correlation in the stock prices is
broken. Pairs trading is one of many techniques that falls into a trading
strategy called
statistical arbitrage
.
8.1.1 Box-Jenkins Methodology
In this chapter, a time series consists of an ordered sequence of equally spaced
values over time. Examples of a time series are monthly unemployment rates,
daily website visits, or stock prices every second. A time series can consist of the
following components:
• Trend
• Seasonality
• Cyclic
• Random
The
trend
refers to the long-term movement in a time series. It indicates whether
the observation values are increasing or decreasing over time. Examples of trends
are a steady increase in sales month over month or an annual decline of fatalities
due to car accidents.
The
seasonality
component describes the fixed, periodic fluctuation in the
observations over time. As the name suggests, the seasonality component is often
related to the calendar. For example, monthly retail sales can fluctuate over the
year due to the weather and holidays.
