V.17:1 (23-26): Seasonal Adjustment Of Time Series Data by George R. Arrington, Ph.D.

Seasonal Adjustment 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 more clearly.

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.