Nelson Mandela said: “We must use time wisely and forever ealize that the time is always ripe to do right.” Time series
data have a temporal order that makes
analysis distinctly different from other
data analysis. The goal of time series
analysis can be divided into characterization or prediction. There is a consistent
pattern contaminated with random noise,
which typically requires filtering to aid
in identifying the underlying pattern. The
pattern itself can be divided into the main
trend and a seasonal component.
The main trend can often be described
by a linear function, which may need to be
transformed to eliminate any non-linear-ity using an exponential or log function.
If there is considerable error masking the
trend, smoothing is required, such as a moving average which replaces components of
the series with a simple or weighted average.
The seasonal component can be examined via autocorrelation correlograms,
which display serial correlation for consecutive lags. A Ljung-Box Q statistic and
Figure 1: A small p-value (< .05) indicates the possibility of non-zero autocorrelation within
the first few lags.
Time Series Diagnostics
The goal of this type of analysis can be divided into characterization or prediction
Time Series Analysis:
Identifying Underlying Patterns