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Time-series forecasting is a forecasting method that uses
a set of historical values to predict an outcome. These historic
values, often referred to as a "time series", are spaced
equally over time and can represent anything from monthly sales
data to daily electricity consumption to hourly call volumes.
Time-series forecasting assumes that a time series is a combination
of a pattern and some random error. The goal is to separate the
pattern from the error by understanding the pattern's trend,
its long-term increase or decrease, and its seasonality,
the change caused by seasonal factors such as fluctuations in use
and demand.
How does CB Predictor work?
CB Predictor, part of Crystal Ball Professional Edition, analyzes the
trend, seasonality, and error in your data and then projects them
into the future to predict likely results. The software can fit
your data to eight different time-series methods, four seasonal
and four non-seasonal, and it will automatically rank those methods
to show you which ones come closest to fitting your data.
But what if your time-series data is dependent on outside influences,
such as weather or regular sales promotions? CB Predictor can perform linear regression, a method of time-series forecasting that
determines the relationship between the dependent variables and
your data and uses that relationship to improve your forecast.
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