Of Time Series Data
by George R. Arrington, Ph.D.
While time series data is the heart of most technical trading
systems, some have a tendency to reflect seasonal patterns;
for example, agricultural commodities tend to follow harvest
cycles. Here’s how to adjust data to see nonseasonal patterns
Time series data, which is data
such as most price and volume
data collected sequentially over
time and usually at fixed inter-vals,
is the basic fuel for most
technical trading systems. As
new data becomes available, it
is used to recalculate the value
of technical indicators. This, in
turn, may trigger a trading sig-nal.
Time series data often con-tains
a bias that reflects sea-sonal
patterns; for example, prices of agricultural commodi-ties
tend to follow harvest cycles and seasonal patterns of
consumption. Many other economic variables, such as em-ployment,
money supply, heating oil demand, and sales of
new automobiles, also exhibit significant seasonal variation.
Often, it is easy to understand why these seasonal varia-tions
occur, but they make it difficult to analyze and under-stand
the time series data. If the price of a wheat futures
contract goes down by 10 cents during August, for instance,
it would be helpful to know how much of that drop can be
attributed to normal seasonal patterns and how much can be
attributed to other factors.
If we use time series data as part of a technical trading
system or to analyze trends, we may want to separate the
normal seasonal variation to see nonseasonal patterns more
clearly. This process is known as seasonal adjustment. Many
widely published economic statistics, such as the unemploy-ment
rate and the consumer price index (CPI), are seasonally
adjusted before being published.