This topic lists and describes time forecasting model algorithms that are suitable for detecting trends in monotonic data:
Behavior 
The algorithm fits a linear regression line using the Minimum Least Squares method, and uses the line to predict future points. The model also calculates the 95% confidence interval for the regression line, displaying the upper and lower bounds. 

Usage 
This algorithm is widely used, because it is suitable for nonperiodic or highgranularity data (for example, day resolution). 
An example of a time forecasting model using the Linear algorithm
Behavior 
A variant of the Linear algorithm that applies outlier filter to input data before estimating the regression. Specifically, the algorithm uses an Mestimation for robust regression. This model also calculates the 95% confidence interval for the regression line, displaying the upper and lower bounds. 

Usage 
This algorithm adds robustness to the Linear algorithm, making it suitable for data with outliers (for example, abnormal peaks). 
An example of a time forecasting model using the Robust Linear algorithm
Behavior 
This algorithm works in two steps:


Usage 
This algorithm is suitable for time series that present a steplike, or sawtoothlike, behavior. These series reflect rapid variations due to configuration changes. For example, when you modify the configuration, for example, to expand a storage volume and instantly allocate more space or to add bandwidth to a network link, the resource utilization data reflects this configuration change instantaneously. This model filters the data before such as configuration change, which is not useful in predicting behavior and trends with the new configuration. 
An example of a time forecasting model using the Robust Linear  Last Ramp algorithm
Behavior 
The algorithm is similar to #Robust Linear  Last Ramp, except that it applies a smoothing filter on historical data after detecting the last ramp in the time series. The smoothing filter consists of a Seasonal Decomposition, after which only the resulting trend component is considered for the prediction. Seasonal Decomposition works using the LOESS method. 

Usage 
This algorithm is also suitable for time series that present a steplike, or sawtoothlike, behavior, and is particularly useful for high variance series and for data containing outliers. 
An example of a time forecasting model using the Robust Linear  Smoothed Last Ramp algorithm
Behavior 
This algorithm uses the Minimum Least Squares method to estimate a seconddegree polynomial fitting on smoothed data obtained with Seasonal Decomposition (as described in #Robust Linear  Smoothed Last Ramp, and uses the line to predict future points. 

Usage 
This algorithm is suitable for time series that reflect nonlinear behavior with a quadratic increase. 
An example of a time forecasting model using the Quadratic algorithm
Behavior 
This algorithm uses the Minimum Least Squares method to estimate a thirddegree polynomial fitting on smoothed data obtained with Seasonal Decomposition (as described in #Robust Linear  Smoothed Last Ramp, and uses the line to predict future points. 

Usage 
This algorithm is suitable for time series that reflect nonlinear behavior with an increase more rapid than quadratic. 
An example of a time forecasting model using the Cubic algorithm
Behavior 
This algorithm uses the Minimum Least Squares method to estimate an exponential fitting on smoothed data obtained with Seasonal Decomposition (as described in #Robust Linear  Smoothed Last Ramp, and uses the line to predict future points. 

Usage 
This algorithm is suitable for time series that reflect nonlinear behavior with an increase more rapid than polynomial. 
An example of a time forecasting model using the Exponential  Multiplicative Trend algorithm
Behavior 
This algorithm works in two steps:


Usage 
This algorithm is ideal for forecasting the underlying mean behavior of data that has significant variation. 
An example of a time forecasting model using the Robust Exponential Damping algorithm
Behavior 
This algorithm is similar to #Robust Linear  Last Ramp, except that the smoothing is done using Exponential Damping. 

Usage 
Similar to Robust Linear  Last Ramp, this algorithm is suitable for time series that present a steplike, or sawtoothlike, behavior. However, models that use this algorithm can result in more pessimistic predictions than models that use the Robust Linear  Last Ramp algorithm. 
An example of a time forecasting model using the Robust Exponential Damping  Last Ramp algorithm