ADAPTIVE EXPONENTIAL SMOOTHING Meaning and
Definition
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Adaptive Exponential Smoothing is a forecasting technique used to predict future values of a time series variable by adjusting the weights of previous observations based on their relative importance. It is a modified version of the traditional Exponential Smoothing method, which assigns equal weights to all past observations.
In Adaptive Exponential Smoothing, the weights assigned to previous observations are adaptively adjusted over time to capture changing patterns or trends in the data. This adaptivity is achieved through a smoothing parameter typically called the learning rate or alpha. The learning rate determines the extent to which recent observations are given more weight compared to older observations.
To calculate the forecast using Adaptive Exponential Smoothing, the method first estimates the initial level and trend of the time series based on the initial observations. Then, it updates these estimates at each time period using a combination of the most recent observation and the previous estimates. The updated estimates are used to forecast future values.
The adaptive nature of this technique allows it to handle time series data with changing patterns, such as seasonality, trends, or sudden shifts in the underlying process. By adjusting the weights dynamically, Adaptive Exponential Smoothing can effectively track and respond to evolving patterns in the time series data, making it a valuable tool for forecasting in dynamic environments.
