Adaptive Exponential Smoothing is a forecasting technique that uses a weighted average of past observations to predict future values. It differs from traditional exponential smoothing in that it adjusts the smoothing parameter based on the level of seasonality and trend in the data, allowing for more accurate and flexible forecasting. This method is particularly useful for time series data with changing patterns and trends.
Adaptive Exponential Smoothing is a statistical forecasting method that is used to predict future values based on historical data. It is an extension of the traditional Exponential Smoothing method, which assigns exponentially decreasing weights to past observations.
The Adaptive Exponential Smoothing method adjusts the smoothing parameters dynamically based on the characteristics of the data being analysed. This allows the method to adapt to changes in the data pattern over time, making it more accurate and reliable in forecasting future values.
The method involves calculating the level, trend, and seasonality components of the data, and then combining them to generate the forecast. The level component represents the average value of the data, the trend component captures the direction and magnitude of any upward or downward movement, and the seasonality component accounts for any regular patterns or cycles in the data.
To determine the optimal smoothing parameters, the Adaptive Exponential Smoothing method uses a combination of optimization techniques and statistical measures. These parameters control the weights assigned to the different components and influence the smoothness of the forecast.
Overall, Adaptive Exponential Smoothing is a valuable tool in forecasting future values based on historical data. Its ability to adapt to changing data patterns makes it particularly useful in situations where the data exhibits non-linear trends or seasonality.
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This glossary post was last updated: 29th March 2024.
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