-Each of these components is briefly described:
-
-Holt-Winters Time Series Forecasting Algorithm is an online, or incremental,
-algorithm that adaptively predicts future observations in a time series. It's
-forecast is the sum of three components: a baseline (or intercept), a linear
-trend over time (or slope), and a seasonal coefficient (a periodic effect,
-such as a daily cycle). There is one seasonal coefficient for each time point
-in the period (cycle). After a value is observed, each of these components is
-updated via exponential smoothing. So the algorithm learns from past values
-and uses them to predict the future. The rate of adaptation is governed by
-3 parameters, alpha (intercept), beta (slope), and gamma (seasonal). The prediction
-can also be viewed as a smoothed value for the time series.
-
-The measure of deviation is a seasonal weighted absolute deviation. The term
-I<seasonal> means deviation is measured separately for each time point in the
-seasonal cycle. As with Holt-Winters Forecasting, deviation is predicted using
-the measure computed from past values (but only at that point in the seasonal cycle).
-After the value is observed, the algorithm learns from the observed value via
-exponential smoothing. Confidence bands for the observed time series are generated
-by scaling the sequence of predicted deviation values (we usually think of the sequence
-as a continuous line rather than a set of discrete points).
-
-Aberrant behavior (a potential failure) is reported whenever the number of
-times the observed value violates the confidence bands meets or exceeds a
-specified threshold within a specified temporal window (i.e. 5 violations
-during the past 45 minutes with a value observed every 5 mintues).
-
-This functionality is embedded in a set of related B<RRAs>. In particular, a FAILURES
-B<RRA> logs potential failures. Presumably a front-end application to B<rrdtool> can
-utilize this B<RRA> to initiate real-time alerts if that is desired.
-
-You can find a detailed description of how to set this up in L<rrdcreate>.
+Here is a brief explanation of these components:
+
+The Holt-Winters time series forecasting algorithm is an on-line (or
+incremental) algorithm that adaptively predicts future observations in
+a time series. Its forecast is the sum of three components: a baseline
+(or intercept), a linear trend over time (or slope), and a seasonal
+coefficient (a periodic effect, such as a daily cycle). There is one
+seasonal coefficient for each time point in the period (cycle). After
+a value is observed, each of these components is updated via
+exponential smoothing. This means that the algorithm "learns" from
+past values and uses them to predict the future. The rate of
+adaptation is governed by 3 parameters, alpha (intercept), beta
+(slope), and gamma (seasonal). The prediction can also be viewed as a
+smoothed value for the time series.
+
+The measure of deviation is a seasonal weighted absolute
+deviation. The term I<seasonal> means deviation is measured separately
+for each time point in the seasonal cycle. As with Holt-Winters
+forecasting, deviation is predicted using the measure computed from
+past values (but only at that point in the seasonal cycle). After the
+value is observed, the algorithm learns from the observed value via
+exponential smoothing. Confidence bands for the observed time series
+are generated by scaling the sequence of predicted deviation values
+(we usually think of the sequence as a continuous line rather than a
+set of discrete points).
+
+Aberrant behavior (a potential failure) is reported whenever the
+number of times the observed value violates the confidence bands meets
+or exceeds a specified threshold within a specified temporal window
+(e.g. 5 violations during the past 45 minutes with a value observed
+every 5 minutes).
+
+This functionality is embedded in a set of related B<RRAs>. In
+particular, a FAILURES B<RRA> logs potential failures. With these data
+you could, for example, use a front-end application to B<RRDtool> to
+initiate real-time alerts.
+
+For a detailed description on how to set this up, see L<rrdcreate>.