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This topic lists and describes time forecasting model algorithms that are suitable for detecting trends in monotonic data:
Behavior  This algorithm works in three steps:


Usage  This algorithm is ideal when a sizable amount of historical data is available, and you detect a clearly periodic behavior in the time series to predict. 
An example of a time forecasting model using the Linear  By Time Shifts algorithm
Behavior  This algorithm uses the HoltWinters Additive model with Exponential Smoothing, explained, for example, in Time series Forecasting using HoltWinters Exponential Smoothing, by Prajakta S. Kalekar, Kanwal Rekhi School of Information Technology, 2004. A confidence band with 90% confidence is also calculated. 

Usage  This algorithm is suitable when the time series shows both trend and additive seasonality. 
An example of a time forecasting model using the HoltWinters algorithm
Behavior  This algorithm works in three steps:


Usage  This algorithm is suitable when the time series has a known seasonality period. It produces particularly effective results on yearly based seasonality when you expect to have similarly shaped data over the course of a number of years. 
An example of a time forecasting model using the Linear  Yearly Time Shift algorithm
Time forecasting model algorithms for detecting trends in monotonic data
Time forecasting model algorithms for detecting trends and seasonal components in data