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Deimantas
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What are the assumptions of exponential smoothing? For example, should autocorrelation of the data be low in order for the exponential smoothing model to be valid, should the process be stationary, etc?
The main assumption of exponential smoothing is that the future value of a variable is dependent on its past values, with more weight given to recent data. This means that the forecast for a time period is a combination of the actual value for that period and the forecast for the previous period.
Exponential smoothing assumes that any seasonality in data is constant and can be removed by adjusting the level parameter. This means that the model will adjust the forecast based on the average difference between the actual values and the forecast values in each season.
The main difference between simple exponential smoothing and double exponential smoothing is that the latter also takes into account the trend in the data. Double exponential smoothing uses a second smoothing parameter to adjust for the trend, making it more suitable for data with a linear trend.
While exponential smoothing can be used for short-term forecasting, it is not recommended for long-term forecasting. This is because the model does not take into account any external factors or changes in the underlying data, which may significantly impact the future values.
Exponential smoothing works best for data with a constant trend and seasonality. It may not perform well for data with irregular patterns or sudden changes. Additionally, it assumes that all past data is equally important, which may not always be the case. Other forecasting methods may be more suitable for specific types of data.